PreprintPDF Available

Abstract and Figures

This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the energy conversion systems transferring the kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning, and recycling. Offshore development and digitalization are also a focal point in this study. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps. Drivetrain in this context includes the whole power conversion system: main bearing, shafts, gearbox, generator, and power converter. The paper discusses current design technologies for each component along with advantages and disadvantages. The discussion of the operation phase highlights the condition monitoring methods currently employed by the industry as well as emerging areas. This article also illustrates the multidisciplinary aspect of wind turbine drivetrains, which emphasizes the need for more interdisciplinary research and collaboration.
Content may be subject to copyright.
Wind turbine drivetrains: state-of-the-art technologies and future
development trends
Amir R. Nejad1, Jonathan Keller2, Yi Guo2, Shawn Sheng2, Henk Polinder3, Simon Watson3,
Jianning Dong3, Zian Qin3, Amir Ebrahimi4, Ralf Schelenz5, Francisco Gutiérrez Guzmán6,
Daniel Cornel6, Reza Golafshan6, Georg Jacobs6, Bart Blockmans7,8, Jelle Bosmans7,8, Bert Pluymers7,8,
James Carroll9, Sofia Koukoura9, Edward Hart9, Alasdair McDonald10, Anand Natarajan11,
Jone Torsvik12, Farid K. Moghadam1, Pieter-Jan Daems13, Timothy Verstraeten13, Cédric Peeters13, and
Jan Helsen13
1Marine Technology Department, Norwegian University of Science & Technology, NO-7491, Trondheim, Norway
2National Renewable Energy Laboratory, Golden, CO 80401, USA
3Technische Universiteit Delft, Mekelweg 2, 2628 CD Delft, The Netherlands
4Leibniz University Hannover, Institute for Drive Systems and Power Electronics, Postfach 6009, 30060 Hannover, Germany
5Center for Wind Power Drives CWD, RWTH Aachen University, Campus-Boulevard 61, 52074 Aachen, Germany
6Institute for Machine Elements and Systems Engineering MSE, RWTH Aachen University, Schinkelstrasse 10, 52062
Aachen, Germany
7KU Leuven, Mechanical Engineering, Division LMSD, Heverlee, Belgium
8Flanders Make, Core Lab Dynamics of Mechanical and Mechatronic Systems, Heverlee, Belgium
9University of Strathclyde, 16 Richmond St, Glasgow G1 1XQ, United Kingdom
10Institute for Energy Systems, School of Engineering, Edinburgh, United Kingdom
11DTU Wind Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
12Equinor ASA, Sandslivegen 90, 5254 Sandsli, Norway
13Department of Mechanical Engineering, Vrije Universiteit Brussel / OWI-Lab, B-1050, Brussels, Belgium
Correspondence: Amir R. Nejad (
Abstract. This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains—the energy
conversion systems transferring the kinetic energy of the wind to electrical energy—in different stages of their life cycle:
design, manufacturing, installation, operation, lifetime extension, decommissioning, and recycling. Offshore development and
digitalization are also a focal point in this study. The main aim of this article is to review the drivetrain technology development
as well as to identify future challenges and research gaps. Drivetrain in this context includes the whole power conversion5
system: main bearing, shafts, gearbox, generator, and power converter. The paper discusses current design technologies for each
component along with advantages and disadvantages. The discussion of the operation phase highlights the condition monitoring
methods currently employed by the industry as well as emerging areas. This article also illustrates the multidisciplinary aspect
of wind turbine drivetrains, which emphasizes the need for more interdisciplinary research and collaboration.
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
1 Introduction
The European Green Deal aims to make the European Union climate-neutral by 2050, with land-based and offshore wind being
an important part to meet this target (EU, 2019, b). The European Union has been at the forefront of wind energy technology
development in recent years, especially offshore—European companies represent an impressive 90% of the offshore global
market (EU, 2019, a). There is a special focus on offshore wind development in the EU Clean Energy for All Europeans5
Package, in which 30% of the future electricity demand, approximately 450 gigawatts (GWs), is expected to be supplied by
offshore wind (EU, 2019, a)—a huge increase from today’s 20 GW of installed capacity (Wind Europe, 2020). In the United
States, it has been estimated that wind can supply 35% of U.S. electricity demand by 2050, with 86 GW installed offshore
(DOE, 2015). Moving from land-based to offshore turbines has also opened possibilities of increasing the size and power of
the wind turbine and plant. Deeper water locations offshore have also been used by floating turbines, with the first floating10
wind plant in operation since 2017. Such fascinating developments are challenging the technological borders and existing
knowledge base of the industry. There is limited experience with such huge machinery in harsh environmental conditions, so
the best practices and standards have not yet fully matured.
The drivetrain converts mechanical to electrical power and transmits the rotor loads to the bedplate and tower. The drivetrain
in this context includes the entire power conversion system from the main bearing to the electrical generator and power conver-15
sion system. The two main drivetrain configurations and components that characterize them are depicted in Figure 1. A variety
of wind turbine drivetrain technologies are available, with pros and cons for each in terms of cost, weight, size, manufacturing,
materials, efficiency, reliability, and operation and maintenance (O&M) (Polinder et al., 2006; Arabian-Hoseynabadi et al.,
2010; Moghadam and Nejad, 2020; Harzendorf, 2021; Harzendorf et al., 2021). With digitalization expanding in all industries,
new opportunities have arisen in the operation phase, including digital O&M and digital twins. As the age of the installed fleet20
continues to increase, considerations for lifetime extension and decommissioning are also becoming more important.
This study reviews the state of the art of the drivetrain technology in the wind turbine industry and discusses future develop-
ment trends. The focus is on conventional and widely used concepts; unconventional designs, such as hydrostatic (Silva et al.,
2014) and hydraulic designs, are not discussed. To achieve the aims of this paper, a life cycle approach (Torsvik et al., 2018), as
illustrated in Figure 2, is employed. First, the design—and, to a limited extent, manufacturing—is discussed. It is followed by25
drivetrain operation—in particular, condition and performance monitoring; and, finally, lifetime extension, decommissioning,
and recycling.
2 Design trends and developments
A compact, lightweight drivetrain is the most cost-effective option for large offshore wind turbines because it reducues the
nacelle mass and hence tower and foundation or floating platform masses and costs. To achieve these reductions, there has30
been a trend toward increasing the mechanical integration of the main bearing, gearbox, and generator (Stehouwer and van
Zinderen, 2016; Demtröder et al., 2019; Nejad and Torsvik, 2021; Reisch, 2021; Zeichfüßl et al., 2021; Weber and Hansen,
2021). In terms of the power conversion system, permanent magnet synchronous generators (PMSGs) with full-power converter
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
With gearbox
Gearbox Main bearing
Generator Power converter
Main shaft
Figure 1. Drivetrain configurations and main components (photos and figures are adopted from Equinor; Guo et al. (2014); OpenPR; Smalley
(2015); Zheng et al. (2020); Siemens Gamesa; DHHI; ABB).
systems are becoming more common than doubly-fed induction generators (DFIGs) with partial-power converter systems.
Concerns over the supply of rare-earth materials typically used in PMSGs have also spurred interest in alternate generator
technologies, such as superconducting generators (Veers et al., 2020). Regardless of drivetrain design choice, the loads and
operational conditions that the drivetrain is subjected to are derived from the design load cases described in the International
Electrotechnical Commission (IEC) 61400-1 design standard for land-based wind and IEC 61400-3-1 and IEC 61400-3-25
design standards for offshore fixed and floating wind applications.
2.1 Main bearing
Current main bearing designs use rolling element bearings (Hart et al., 2020). Because of the large applied nontorque loads, the
resulting bearing designs are likewise large in diameter, limited in size only by manufacturing and transportation restrictions.
Continued use of rolling element bearings is likely because it is a familiar technology, albeit with design trends moving outside10
the envelope of prior experience. A central driver behind the move to large-diameter rolling element bearing arrangements is
the need for cost-effective rotor support solutions. Other existing bearing technologies—such as hydrostatic, air, and magnetic
bearings—tend to require very rigid support structures or are limited to smaller diameters than would be required by modern
wind turbines, although hybrid solutions combining different technologies have been proposed (Shrestha et al., 2010). Given
the continued increases in main bearing diameter, understanding the effects of deflections in large-diameter rolling element15
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Figure 2. Wind turbine life cycle (Torsvik et al., 2018).
bearings is essential, and the current practice of assessing bearing design life through only the conventional calculation methods
in the International Organization for Standardization (ISO) standards 76 and 281 and technical specification (TS) 16281 might
be insufficient with respect to the resulting service life observed in operation. Additionally, main bearings experience repeating,
large-scale fluctuations in load, even during normal operations (Hart, 2020). These fluctuations likely increase the risk of
other damaging mechanisms (such as roller skidding, surface fatigue, wear, and abrasion) not accounted for in fatigue-life5
calculations. As such, the analysis of the operating conditions of these components has further indicated that current life-
assessment standards might be insufficient. Main bearing failure rates of up to 30% during a 20-year design life have also been
reported (Hart et al., 2019). Further work is therefore needed to identify principal drivers of main bearing failures, allowing for
the development of appropriate design standards and best practices specific to this component, which, in turn, would lead to
improvements in reliability.10
Modern offshore wind plants are high-value assets, and there is an increasing interest in longer design life and lifetime
extension. Turbine size and drivetrain arrangement can, however, result in main bearing replacement becoming more difficult
and expensive, generally requiring removal of the rotor. Consequently, main bearings will increasingly be regarded as part of
the load-carrying structure, with cost implications of failure more severe as a result. A further ramification of increased levels
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
of integration is that main bearing operational requirements become linked to those of other components. For example, in
addition to supporting the turbine rotor, some direct-drive configurations require the main bearing to also support the generator
rotor while maintaining an appropriate generator air gap. Coupled approaches to the modeling and assessment of wind turbine
drivetrain systems will therefore become increasingly important.
Novel main bearing design concepts are also being developed and tested, including asymmetric spherical roller bearings5
(Loriemi et al., 2021), field-replaceable main bearings, and plain bearings (Rolink et al., 2020, 2021).
2.2 Gearbox
Wind turbine gearboxes continue to increase in size (up to 3 m in diameter) and power (up to 15 megawatts (MWs)) (Vaes
et al., 2021). With multistage gearboxes using four or more planet epicyclic systems, torque densities of 200 newton-meters
per kilogram and speed increasing ratios up to 200 are now available (Daners and Nickel, 2021). To achieve further cost10
reductions through economies of scale, modular gearbox designs have been introduced (Windpower, 2021). Gearboxes are
designed for a minimum of a 20-year life, as specified in the IEC 61400-4 and American Gear Manufacturers Association
(AGMA) 6006 gearbox design standards. Provisions for up-tower service or replacement of gearbox components is becoming
more common and is required for components that have a design life less than the gearbox. The gearbox system comprises
many elements (primarily the rotating shafts, gears, and bearings), so the reliability of the gearbox is the product of the15
reliability of all the failure modes for which there exists a reliability calculation. But many, if not most, of the failure modes
experienced in operation do not have a standardized reliability calculation; hence, as described earlier, there exists a difference
between the apparent reliability observed in operation and the calculated design reference reliability. This is not unusual, and it
occurs in other industries, although the O&M cost impact for wind turbines can be more severe. For gearboxes, the reliability
calculation considers gear tooth surface durability (pitting) according to ISO 6336-2 and bending strength according to ISO20
6336-3, rolling element bearing rating life from subsurface-initiated fatigue (i.e., rolling contact fatigue) according to ISO
281 and ISO/TS 16281, and shaft fatigue fracture according to Deutsches Institut für Normung 743 and American National
Standards Institute (ANSI)/AGMA 6001. In some cases, a safety factor for or percentage risk of these failure modes can at
least be quantified in the gearbox design process, including gear tooth scuffing according to ISO/TS 6336-20, ANSI/AGMA
925, and ISO/TS 6336-21; and gear tooth micropitting according to ISO/TS 6336-22; or otherwise assessed for gear tooth25
flank fracture according to ISO/TS 6336-4. Safety factors for the static strength of gears and bearings is calculated according to
ISO 6336 and ISO 76, respectively. Other bearing failure modes, such as surface-initiated fatigue (e.g., micropitting), adhesive
wear, corrosion, electrical damage, and white-etching cracks can only be assessed qualitatively. Requirements for materials,
processing, and manufacture are part of these standards. Further in-depth contact and finite element analysis are used to design
the microgeometry of these rotating components, provide additional rating life calculations (Morales-Espejel and Gabelli,30
2017), and analyze the supporting housing structures. In addition to classical reliability approaches, use of structural reliability
methods for reliability analysis of gears has also been investigated (Nejad et al., 2014a; Dong et al., 2020). Design guidance
for the use of plain bearings in the gearbox is under development because they offer advantages in terms of torque density
and are life-limited only by wear, although they are already becoming common in new gearboxes (Weber and Hansen, 2021;
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Zeichfüßl et al., 2021). Surface engineering, lubricants, and lubrication of the gearbox also play an essential role in gearbox
design, operation, and reliability (Dhanola and Garg, 2020; Jensen et al., 2021).
2.3 Generator
As highlighted earlier, wind turbine drivetrains can be either geared or direct-drive generator systems (Polinder et al., 2013).
The geared generator system can be further divided into either a DFIG with partial power converter or a brushless generator5
with full power converter (GFPC) system. The DFIG system has been the most popular topology for medium-size turbines
ranging from 3-6 MW. The GFPC system uses either a squirrel cage induction generator or a PMSG. It aims to achieve a
trade-off between generator size and maintenance effort. Many manufacturers now provide commercial GFPC solutions at
power levels up to 10 MW (Siemens, 2020; ABB, 2020). In terms of direct-drive systems, rare-earth PMSGs are appealing
for offshore applications. The mainstream power level is from 5–7 MW, but the top power level has kept increasing during the10
past two decades.
The stator elements of DFIGs and PMSGs are largely the same. The major difference in terms of the electrical machine
hardware is the rotor design and the means—-or lack thereof—-of getting current onto and off the rotor. These differences can
impact both efficiency and failure mechanisms and their rates. In terms of efficiency, induction generators use a set of currents
on the rotor to produce the rotor magnetic field. This leads to Joule rotor losses and hence a decrease in efficiency. In contrast,15
a PMSG uses rare-earth permanent magnets to produce the rotor magnetic field, hence avoiding further Joule losses.
In terms of failure types, a DFIG uses carbon brushes and slip rings to conduct the currents between the rotor and the stator.
The brushes typically wear out over time and need frequent inspection and replacement. The PMSG avoids those elements.
There are also differences in reliability because of the presence of conductor and insulation systems (DFIG) and magnet
materials (PMSG), but those are not yet clear. A comparative study of DFIGs and PMSGs showed that during the early life, a20
PMSG has a failure rate 40% lower than that of a comparable DFIG (Carroll et al., 2014).
Alongside this variation in the reliability of different electrical machine architectures, there is variation in the reliability
caused by the torque rating of the generator. This was first shown by Spinato et al. (2009). For example, it is possible to
conceive of two wind turbines that both use the same generator type, but one is in a geared configuration (with a gearbox
ratio of 100), and the other is direct drive. For the same wind turbine rotor (and subsequent power and rotational speed), the25
torque rating of the direct-drive generator will be 100 times more than that of the geared generator. Assuming that the same
electromagnetic shear stress is produced by the two PMSGs, the volume of the direct-drive generator will also be 100 times
that of the higher speed PMSG. If they have the same ratio of diameter to axial length, then the generator diameter will be
100 (i.e., ×4.64) that of the geared machine. The electromagnetic materials are approximately proportional to the surface
area of the rotor and stator. In the case of the low-speed machine, this might be ×4.642(i.e., 21.5) times that of the higher30
speed machine. With more poles, more coils, longer conductors, and insulation, it is likely that the failure rate is higher in
direct-drive machines if there is no improvement in failure rate intensity.
The reliability and availability of the wind generator system has a decisive impact on the cost of energy (COE), especially
offshore (Carroll, 2016; Shipurkar et al., 2016). The generator design should consider the interactions with other components to
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
improve the system reliability of the drivetrain (Moghadam and Nejad, 2020). In large wind turbines, multiphase windings with
modular converters can be used to improve the generator system availability (Shipurkar et al., 2015; McDonald and Jimmy,
Upscaling is still a continuing trend for both land-based and offshore wind turbines because a higher power wind turbine
system leads to a lower COE (Sieros et al., 2012). For land-based wind, recent developments include the design of 6-MW5
and 8-MW turbines, which will be on the market in the near future, whereas offshore commercial applications now aim for
10–15 MW, and research is going beyond 15 MW (Gaertner et al., 2020; Ashuri et al., 2016; Sartori et al., 2018). Upscaling
brings many challenges, including large generator weight, high manufacturing/installation difficulties and cost, complicated
electromechanical dynamics, and complexity of system monitoring. A systematic design approach will be required for the
design of the generator, where cooling and efficiency will be among the challenges in higher power. Depending on the type,10
nominal power, speed, the specific electric loading, and subsequently the armature thermal loading, three general solutions—
namely, air-air, air-water, and water jackets—are commercially available to implement the cooling system of a wind generator
(Polikarpova et al., 2014). Significant cost savings can be realized with the development of a more effective stator winding
cooling system that further limits the current density to enable the development of higher power PMSGs of substantially
smaller diameters while not adversely affecting the electromagnetic performance of the generator.15
Multiphase, modular designs are solutions to tackle some of the challenges, and they have been used in commercial sys-
tems (Yaramasu et al., 2015; McDonald and Bhuiyan, 2016). Concerns over the availability of rare-earth elements—such as
neodymium, praseodymium, and dysprosium—typically used in PMSGs have led the wind and other industries to develop
innovative technologies to reduce, substitute for, or entirely eliminate their need in generators (Veers et al., 2020). This also
results in technologies that are lighter than PMSGs. Breakthroughs in superconducting materials could change the scenario of20
materials and upscaling completely (Hoang et al., 2018), with several superconducting generators successfully tested (Frank
et al., 2003; Bergen et al., 2019) or in development (Moore, 2020).
The interactions between the generator and power electronics can bring issues including bearing currents, additional stress
in insulation because of overvoltage in transients, and high-voltage slew rates (Chen et al., 2020); therefore, these interactions
should be studied and modeled not only for the design but also for O&M. Proper filters and control methods should be integrated25
according to the generator types and power electronics topologies. For the upscaling of wind generators, various modular and
multilevel power converter topologies feeding multiphase windings will be a promising solution. But attention should be paid
to circulating current and potential asymmetric supplies to avoid risks (Yaramasu et al., 2015).
2.4 Power converter
The power converter is responsible for controlling the output power of the generator with regulated voltage and frequency30
(Moghadam et al., 2018). Wind turbine power converters used to have a topology as shown in Figure 3, top, where the generator-
side converter is a diode rectifier cascaded with a boost converter to maintain a stable direct current (DC) link voltage, then a
two-level inverter is employed on the grid side to ensure full control of the grid current injection (e.g., total harmonic distortion
and power factor). This topology has a relatively low cost because of fewer power switches than newer configurations, so
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
it is widely used for generators in small- to medium-size wind turbines. For megawatt-scale generators, however, the low-
frequency torque pulsation and high total harmonic distortion become very harmful to the generator. As a result, the design of
the boost converter becomes very challenging, and therefore the front end is then replaced by a two-level, six-switch power
factor correction, which is shown in Figure 3, bottom, and is called a back-to-back (BTB) converter. A DFIG with partially
loaded BTB converter is commonly used for generators less than 3 MW, whereas a PMSG with a fully loaded BTB converter5
is commonly used for generators greater than 3 MW.
Figure 3. Typical wind power converter topologies from the kilowatt to megawatt scale (Blaabjerg and Ma, 2013). Top: Diode rectifier +
boost DC/DC + two-level voltage source converter (VSC). Bottom: Two-level back-to-back converter.
When the power rating is 10 MW or more, a single two-level BTB converter is no longer suitable because the current stress
of the power devices would be extremely high. For two-level BTB converters, the grid-side voltage is typically 690 V. To
solve this, multiple two-level BTB converters can be connected in parallel to share the current, whereas the connection of the
generator side can be slightly different depending on whether the generator has a multiwinding (see Figure 4, top). Another10
way to upscale the power rating is to increase it to medium voltage, for instance, by a neutral-point-clamped (NPC) converter
(see Figure 4, down) or a modular multilevel converter.
Other crucial topics for power converters include thermal loading and grid integration. Intermittent winds create temperature
swings in power converters, which are the main factor of power converter aging. The possibility of applying energy storage
systems to smooth wind power has been investigated to reduce this aging (Qin et al., 2013). The approach is effective, and the15
stress mitigation performance is affected by the energy and power rating of the energy storage system; however, the energy
storage system’s high cost is still the main barrier preventing its widespread application. Variable switching frequencies can
also somewhat reduce temperature swings (Qin et al., 2015b), which is an attractive option as a control approach that does
not add hardware cost. Nonetheless, the grid filter design to handle the variable switching frequency might be challenging. A
promising approach can be using the kinetic energy in the wind turbine’s rotor as energy storage by rotating the speed control20
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Figure 4. Beyond 10-MW wind power converter topologies. Top: Two-level BTB VSC in parallel. Bottom: Three-level NPC converter.
to suppress the power fluctuation in the power converter and thereby reduce the temperature swings (Qin et al., 2015a). Another
topic that is trending for the wind power converter is grid integration. Grid-supporting mode control was applied in the grid-side
converter, and it is still mainstream; however, as wind power penetration is increasing, the grid is becoming relatively weak
(low short-circuit ratio, low inertia, etc.). Grid-supporting mode control can cause power quality issues and even grid failures
in some severe cases (Larumbe et al., 2018, 2019). Energy storage systems or synchronous condensers can be associated with5
wind plants to provide inertia and to reduce the burden of the grid without grid reinforcement (which is very expensive), but
these components are still expensive. Another promising approach is to apply grid-forming mode control to the grid-side wind
power converter, so the inertia in the wind turbines can be used to support the grid and enhance grid stability and reliability.
There is a trend in power electronics to evolve from silicon-based power semiconductors to wide-bandgap devices (e.g.,
silicon-carbide devices). This will have a positive impact on wind energy systems because it can improve the power den-10
sity and improve the efficiency of the power converters (Erdman et al., 2015). In the meantime, it also brings challenges to
wind generators because of the high-voltage slew rate as a result of fast switching. Proper filtering and oscillation damping
technologies should be used to mitigate the side effects, such as common mode current and insulation degradation.
The converter choice of cooling system depends on the nominal power and voltage, power density and thermal design, and
generator technology, which can be based on either air or direct/indirect liquid cooling (Zhou et al., 2013). By choosing a liquid15
cooling system, the size of the converter for high-power applications (>5 MW) can be significantly reduced.
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
2.5 Modeling and analysis
Wind turbine drivetrains are subject to dynamic loading from a wide range of operating conditions caused by wind shear, veer,
turbulence, and gusts; changes in the turbine operational state; grid faults; and nacelle motions; therefore, it is essential that
the computational models for the drivetrain consider the dynamics of the rotor and the demands of the grid through the con-
verter. Such an electromechanical model captures the aeroelastic interactions of the rotor and characterizes the voltage/current5
excursions in the generator because of grid requirements (Gallego-Calderon et al., 2017; Bruce et al., 2015; Blockmans et al.,
Given the complex contact mechanics, one of the most challenging aspects of simulating wind turbine gearboxes is modeling
the meshing gear teeth and supporting bearings. With their computational efficiency, lumped-parameter methods have been the
method of choice for modeling gears and bearings in system-level, flexible, multibody systems. In this approach, a pair of10
meshing gears is simplified into a pair of rotating cylinders with constant inertias that are interconnected by a (nonlinear and/or
time-varying) spring along the line of action or contact. Analogously, in lumped-parameter bearing modeling, the contact of the
rolling elements with the inner and outer bearing raceways are described in terms of nonlinear springs. For meshing gear teeth,
the tooth stiffness changes periodically as the number of teeth in contact and the contact locations along the active tooth flanks
change throughout the gear rotation, whereas for rolling element bearings, the stiffnesses vary with the magnitude and the15
location of the rolling element contact loads. The various lumped-parameter models differ primarily in the way the contact is
computed. One of the oldest but most complete approaches for modeling the interactions between contact surfaces is the classic
contact theory by Hertz and its derivatives (Johnson and Johnson, 1987). Hertzian contact theory is valid i) when the contact
area is sufficiently far from the boundaries of the contacting bodies, allowing them to be treated as elastic half spaces; and ii)
when the elastic deformation of the body is confined to the contact zone. These assumptions have proven to be particularly valid20
approximations in bearing analysis, where the direct application of Hertzian contact theory has lead to computationally efficient
and accurate three-dimensional ball bearings (De Mul et al., 1989a; Lim and Singh, 1990) (see Figure 5a). For gears, however,
the Hertzian assumptions are not consistent with the comparatively large bending, compressive, and shear deformations that
are typically encountered in meshing gears. Lumped-parameter gear modeling techniques are therefore typically formulated
using mesh stiffnesses that are obtained through empirical formulations (Cai and Hayashi, 1994), analytic techniques based on25
a combination of linear elasticity theory and Hertzian contact theory (zu Braunschweig. Institut für Maschinenelemente, 1951;
Wang et al., 2018), or polynomial curve fits that are derived from finite element simulations (Kuang and Yang, 1992). Note that
these mesh stiffnesses are generally formulated per unit of face width and thus assume a uniform contact force distribution along
the face width. In helical gears, however, the contact forces are nonuniformly distributed across the tooth face width, depending
on the gear microgeometry, the helix angle, and possible gear misalignments. Although three-dimensional lumped-parameter30
models have been formulated for helical gears (Eritenel and Parker, 2012), a more common approach in wind turbine gearbox
modeling—-as is also available in a number of commercial, flexible, multibody simulation software packages—-is to divide
each helical gear into a number spur gear slices and to sum the stiffnesses and/or contact forces of the individual slices (Feng
et al., 2018) (see Figure 5b). A similar slicing approach is applied in lumped-parameter roller bearing modeling, where the
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
contact force distribution over the roller surfaces is a nonuniform line load (De Mul et al., 1989b). Note that although lumped-
parameter approaches yield reasonably accurate and efficient ball and roller bearing models, contrary to lumped-parameter gear
contact models, these bearing models are rarely directly integrated into system-level wind turbine drivetrain models; instead,
the models are used to derive linearized stiffness matrices that describe the three-dimensional behavior of the bearing at a
selected operating point (Helsen et al., 2011).5
Figure 5. (a) Lumped-parameter bearing model based on Hertzian contact theory; (b) helical gear slicing for three-dimensional lumped-
parameter gear analysis.
Although lumped-parameter models enable the rapid construction and efficient evaluation of gear and bearing models, they
lack the modeling complexity required to evaluate dynamic behavior with, for example, gear geometric modifications or hous-
ing flexibility. This is desirable, e.g., in the analysis of planetary gear sets, where the flexibility of the ring-housing assembly
has a considerable impact on the overall gearbox behavior (Hu et al., 2019). In distributed parameter methods, such as the finite
element method, the full geometric extent of the gear pair is considered, whereas a large part of the lumped-parameter assump-10
tions are replaced by first principles. The increased accuracy of these methods comes at the price of an increased computational
cost that is compatible only with static simulations (e.g., computing a linear bearing stiffness matrix (Guo and Parker, 2012)
or the stiffness maps of a pair of gears (Palermo et al., 2013)). To alleviate the computational burden of the finite element
method in dynamic simulations, two approaches have recently been introduced. The first approach combines the finite element
method with semi-analytic results from classic contact theory to eliminate the need for highly refined finite element meshes15
in the zone of contact (Andersson and Vedmar, 2003; Vijayakar, 1991). The second approach reduces the number of degrees
of freedom in the finite element models by applying model order reduction techniques that are specifically tailored toward
dynamic contact problems (Blockmans et al., 2015; Fiszer et al., 2016). Given the complexity of these methods, however, the
usage of finite element method-based techniques to model gears and bearings in system-level drivetrain simulations remains
largely restricted to the preprocessing phase of the simulation or limited to static simulations. In the absence of complex contact20
interactions, the modally condensed finite element method (Craig Jr and Ni, 1989) becomes a practical means for modeling
complex, flexible components such as planet carriers, housings, and the bedplate that exhibit relatively low-frequency modal
behavior; whereas shafts are commonly represented by Timoshenko beam elements (Struggl et al., 2015). With the increasing
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
size of wind turbines, the flexibility of these components becomes increasingly important because it can significantly affect
internal load distributions and vibrations (Helsen et al., 2012). Components with high stiffness-to-mass ratios, on the other
hand, are modeled as rigid bodies where the number of degrees of freedom equals six minus the number of applied motion
constraints. Couplings such as universal and revolute joints are typically represented by algebraic constraint equations, whereas
interference fits, spline couplings, and bolted connections are commonly idealized into perfectly homogeneous rigid connec-5
tions (Marrant et al., 2010). Another modeling approach for spline couplings is to consider rigid in rotation but soft in tilting
directions (Guo et al., 2016).
Although significant strides have been made in recent years to increase the accuracy of wind turbine gearbox simulations,
a number of challenges remain. With regard to gear and bearing simulation, the modeling of contact damping phenomena is
not nearly as effective and well understood as the modeling of stiffness-related effects despite meritorious contributions in this10
direction (Li and Kahraman, 2013). In addition, the same techniques that resulted in effective lumped-parameter gear contact
models have failed to achieve similar results in the field of spline modeling. This is largely because of the relatively large
dimensions of the contact zone, rendering Hertzian techniques inaccurate and slicing techniques impractical. With unreduced
finite element-based approaches (Kahn-Jetter and and Wright, 2000) as the main resort, simulations often idealize spline
couplings, which can significantly impact the contact load distributions, especially in planetary gear stages. Finally, with the15
number of gearbox modeling approaches continuously increasing in both the scientific literature and in commercial software,
there is a need for identifying and validating the required levels of modeling accuracy in gearbox analyses, including forward
dynamic analyses (He et al., 2019), durability analyses (Ding et al., 2018), transfer path analyses (Vanhollebeke et al., 2015),
and inverse analyses (Bosmans et al., 2020).
3 Operation and maintenance20
O&M costs represent a sizable and potentially increasing share of the COE, especially as wind’s COE declines because of
reduced upfront costs and improved performance. Recent data suggest that O&M can account for 25% to more than 35% of
wind’s COE (Carroll et al., 2017; Wiser et al., 2019). Downtime must also be considered. Additionally, increased availability
might not lead to reduced O&M costs offshore because vessel type costs must also be considered for turbine repair. For
example, crew transfer vessels have much lower costs than jack-up vessels.25
The maintenance paradigm is shifting from reactive, periodic time or usage-based maintenance to condition, reliability-
centered (or predictive) maintenance supported by digital twins. If the turbines do not have dedicated condition monitoring
systems, some anomaly detection or fault diagnosis can be conducted based on turbine supervisory control and data acquisition
(SCADA) system data, for which the main purpose is operational control.
3.1 Condition monitoring and fault detection30
Condition monitoring is an umbrella term that spans various different ways of tracking the health state of a machine where
typically vibration, temperatures, oil contamination, or electrical signatures from the generator are used as input signals (Fu
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
et al., 2017; Qiao and Qu, 2018). By using appropriate analysis methods, system changes caused by damaged components
(e.g., flaking of bearing raceways) or faulty system states (e.g., water contamination) can be identified. A recent review of the
condition monitoring of drivetrains is offered by Helsen (2021). The following sections discuss the two most commonly used
types of condition monitoring for wind turbine drivetrains along with the acoustic emissions approach.
3.1.1 SCADA-based condition monitoring5
Wind turbine SCADA systems produce hundreds of channels of data concerning the operation of a turbine. In reality, only
a small fraction of these data provide valid information that can be used for condition monitoring, and there is a significant
challenge in how to extract which information is important. SCADA data in wind turbines are typically sampled at either a 10-
minute or a 1-second interval. From a condition monitoring point of view, these data can serve as a low-cost potential solution
because no extra sensors are required. The entire list of parameters tracked in the SCADA data is typically quite extensive, but10
an overview of basic SCADA parameters is given in Table 1. Normally, the extent and quality of the SCADA data depend on
the turbine manufacturer. Other possible uses besides condition monitoring include power curve analysis (Lydia et al., 2014)
and modeling with, e.g., k-nearest neighbors (Kusiak et al., 2009), spare part demand forecasting (Tracht et al., 2013), and load
monitoring (Wächter et al., 2015). Angular velocity measurements from SCADA have also been used for fault detection and
remaining useful life (RUL) (Nejad et al., 2014b, 2018; Moghadam and Nejad, 2021, 2022).15
Table 1. Overview of some basic SCADA parameters (based on (Tautz-Weinert and Watson, 2016; Yang et al., 2013, 2014; Godwin and
Matthews, 2013; Garcia et al., 2006; Zaher et al., 2009; Catmull, 2011; Watson et al., 2011; Schlechtingen et al., 2013; Wilkinson et al.,
2014; Sun et al., 2016)).
Category SCADA parameter
Environmental Wind speed, wind direction, ambient temperature, nacelle temperature
Electrical Active power output, power factor, reactive power, generator voltages, gener-
ator phase current, voltage frequency
Control variables Pitch angle, yaw angle, rotor shaft speed, fan speed/status, generator speed,
cooling pump status, number of yaw movements, set pitch angle/deviation,
number of starts/stops, operational status code
Temperatures Gearbox bearing, gearbox lubricant oil, generator winding, generator bear-
ing, main bearing, rotor shaft, generator shaft, generator slip ring, inverter
phase, converter cooling water, transformer phase, hub controller, top con-
troller, converter, controller, grid busbar
To analyze SCADA data, machine learning techniques are often employed. One classification of machine learning tech-
niques for wind turbine condition monitoring is to divide them into supervised (classification and regression) and unsupervised
(clustering) learning. An example of classification for fault detection, isolation, and failure mode diagnosis on the gearbox
is illustrated by Koukoura et al. (2019), whereas Turnbull et al. (2019) applied a combination of clustering and classification
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
techniques to group similar operating conditions and detect generator faults. An example of regression to detect anomalies in
vibration indicators can be found in Verstraeten et al. (2019). The authors use Bayesian ridge regression to fit linear parameters
and inherent noise to the observed data while maintaining the uncertainty over the parameters. This way the model can distin-
guish between expected and anomalous behavior while capturing the stochasticity of the parameters. In Helsen et al. (2018),
an ensemble of models is used to classify bearing temperature data in normal and anomalous behavior. An extensive review of5
machine learning approaches applied to wind turbines can be found in Stetco et al. (2019).
Other researchers, for example, Tautz-Weinert and Watson (2016), categorize the different SCADA-based monitoring meth-
ods into five classes: trending, clustering, normal behavior modeling (NBM), damage modeling, and assessment of alarms and
expert systems.
i. Trending A very straightforward approach is to monitor the SCADA parameters over a long period of time and to use10
statistical thresholds for alarming.
ii. Clustering When large numbers of wind turbines need to be monitored efficiently, it becomes imperative to have
an automatic manner to classify the turbines as “healthy” or “faulty.” Examples of clustering can be found in Kusiak and
Zhang (2010), where drivetrain and tower accelerations are analyzed using SCADA data by means of a modified k-means
clustering conditioned on the wind speed. Kim et al. (2011) and Catmull (2011) applied self-organizing maps instead of k-15
means to build such clusters. Despite these advancements, the interpretation of the clustering results is often still perceived to
be difficult (Tautz-Weinert and Watson, 2016).
iii. NBM NBM employs the same idea of anomaly detection as the previous techniques, but it focuses more on the
empirical modeling of the measurements. The residual error between the modeled and observed parameter then serves as a
health indicator. A basic example of NBM involves the use of linear and polynomial models. A linear autoregressive model20
with exogenous inputs was used by Garlick et al. (2009) to detect generator bearing failures from the bearing temperature.
Higher-order polynomial full signal reconstruction models of drivetrain temperatures were developed by Wilkinson et al.
(2014) to detect gearbox and generator failures.
iv. Damage modeling Instead of training empirical normal behavior models, the measurements can be interpreted with
physical models to improve the accuracy. Gray and Watson (2010) developed a damage model using physical failure modes25
of interest to estimate the failure probability. A general scheme for a physics-based monitoring approach was proposed by
Breteler et al. (2015) .
v. Assessment of alarms and expert systems The last class looks at the outputs of the SCADA control alarms or the NBM
output alarms. A typical example of this class is the analysis of the status codes of the wind turbine. Status code processing
approaches typically investigate the possibility of extracting useful, actionable information about the health of the turbine from30
these status codes, and there exist many different ways to do this. For example, Chen et al. (2012) used a probabilistic approach
with Bayesian networks to track down root causes for failures such as a pitch fault. Qiu et al. (2012) also used Bayes’ theorem
and compared the extracted patterns using a Venn diagram. Other approaches often involve machine learning, such as Kusiak
and Li (2011), who used neural network ensembles to predict status codes and their severity to detect a malfunction. Last, a
significant amount of research examines the use of “expert” systems to interpret the status codes or model outputs. Often these35
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
systems are based on using fuzzy logic to determine a diagnosis for anomalies. Example research works that are based on or
employ fuzzy logic are given in (Garcia et al., 2006; Schlechtingen et al., 2013; Sun et al., 2016; Cross and Ma, 2015; Li et al.,
2013, 2014).
3.1.2 Vibration-based condition monitoring
In general, vibration-based condition monitoring is by far the most prevalent and widely used method, largely because of its5
ease of instrumentation and its reliable response to damage development (Randall, 2011). First, the majority of all vibration
signal processing techniques has some requirement of stationarity. In most cases, stationarity in time is required for harmonic
frequencies such that spectrum-based approaches are not invalidated because of frequency smearing. Wind turbines, however,
are far from stationary machines because the wind dictates the rotation speed of the rotor. This speed fluctuation leads to time-
varying harmonic frequencies of the vibration sources, such as gears, shafts, or bearing. Knowledge of the speed is therefore10
crucial for many signal processing methods because this speed fluctuation needs to be compensated or considered. A common,
accurate, and reliable way to gain this speed information is through the installation of an angle encoder or tachometer on one
of the rotating shafts in the gearbox. An alternative is to estimate the instantaneous angular speed directly from the vibration
signal itself. An overview and comparison of the state of the art in vibration-based rotation speed estimation can be found
in Peeters et al. (2019); Leclère et al. (2016).15
From a statistical point of view, gear vibration signals are considered deterministic because the gears are locked in place
and do not exhibit random slippage like bearings. On the other hand, bearing vibrations are regarded as stochastic in nature
because of the random slippage of the roller elements, and they are normally characterized as being second-order cyclosta-
tionary, meaning they have a periodic autocorrelation. This distinction in statistical characteristics provides an opportunity for
signal processing methods to separate gear from bearing signals; therefore, a common follow-up step to angular resampling20
is employing a signal separation technique, such as discrete/random separation (Antoni and Randall, 2004b), self-adaptive
noise cancellation (Antoni and Randall, 2004a), linear prediction filtering (Sawalhi and Randall, 2004), the (generalized) time
synchronous average (Abboud et al., 2017, 2016), or cepstrum editing (Peeters et al., 2018). Alternative signal separation
methods—such as (ensemble) empirical mode decomposition (Huang et al., 1998; Wu and Huang, 2009), principal component
analysis, or variational mode decomposition(Dragomiretskiy and Zosso, 2013)—can isolate signal subspaces in the vibration,25
but these subspaces are typically not guaranteed to have any relevance to mechanical components because they do not employ
any prior physical knowledge.
After preprocessing the vibration signal, the last step involves the identification of potential faults. Current practice in
condition monitoring systems often revolves around tracking time-domain statistical indicators (de Azevedo et al., 2016; Lu
et al., 2009; Tchakoua et al., 2014). Examples of some commonly used time-domain indicators are given in Ali et al. (2018);30
Zhu et al. (2014); D’Elia et al. (2015); Veˇ
r et al. (2005); Rai and Upadhyay (2016); Sharma and Parey (2016); Decker et al.
(1994); Zakrajsek et al. (1993); Bozchalooi and Liang (2007). These time indicators can all be used to characterize trends in
measured vibration signals.
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
The main reason why spectral methods are so popular in condition monitoring is that they allow for not only fault de-
tection but also fault diagnosis. For gears, approaches for fault detection usually focus more on tracking the amplitudes of
harmonics and sidebands in the spectrum or the cepstrum, whereas for bearings more cyclostationarity-based methods tend to
be employed. Nonetheless, for gear faults it is also recommended to look at the cyclostationary signature of a signal because
distributed gear faults can significantly impact the modulation of the deterministic gear signals. A local fault on one of the gear5
teeth will introduce low-level sidebands in the spectrum, whereas distributed gear damage exhibits higher-level sidebands.
The presence of modulation sidebands is also the main reason why cepstrum-based techniques are popular for gear diagnos-
tics. Because the cepstrum groups together equally spaced harmonics, it provides a very effective means to track the average
amplitude of the sidebands. A summary of methods applied for the diagnosis of a faulty gearbox is presented in Sheng (2012).
The most popular approach for the analysis of second-order cyclostationary signals (and, correspondingly, for bearing diag-10
nostics) is envelope analysis. The envelope of a signal is considered to be any function that “encloses” the energy variation in
the signal. By taking the modulus of the analytic version of the signal, obtained through the Hilbert transform, the envelope
time waveform can be found (given that the envelope frequency of interest respects Bedrosian’s theorem (Bedrosian, 1963)).
Usually the envelope is squared (effectively done by multiplying the analytic signal with its conjugate) before taking the Fourier
transform to analyze its envelope spectrum (Ho and Randall, 2000).15
3.1.3 Acoustic emissions condition monitoring
To detect strong nonstationary signals such as sudden crack propagation, using acoustic emissions could be a suitable solution.
This technology has already been proven for crack detection on pressure tanks, and its applicability for monitoring of rotating
components is being researched with promising results. For example, rolling bearings have been exposed to critical operating
conditions, such as high-friction lubrication regimes, overloading, or high angular accelerations, which were successfully de-20
tected and differentiated using an acoustic emissions-based detection scheme (Cornel et al., 2018). Further, acoustic emissions
have been successfully used for the detection of subsurface cracks in bearings with a response time up to 55% earlier than
classic vibration-based detection. Further investigations are being carried out to assess the economic value of this earlier re-
sponse. Nonetheless, there are still several challenges to be addressed, such as filtering ambient noise or differentiation between
individual and overlapping signals of individual components, such as bearings, gears, and couplings.25
A further complex but promising challenge of bearing condition monitoring is linking different data acquisition techniques,
such as monitoring acoustic emissions and electrical effects or using SCADA data (de Azevedo et al., 2016). To overcome this
and the aforementioned challenges, two key aspects need to be addressed in further research:
Fault diagnosis algorithm: the distinct feature extraction of specific fault mechanisms in real-world applications in non-
stationary operating conditions, including the integration between condition monitoring systems; the estimation of the30
RUL; and new methods of signal analysis, such as machine learning
Sensor selection and placement: the distinct, transferable description of the influence of fault mechanism specific com-
ponents inside a complex mechanical system on the system’s vibration behavior.
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Further, each gearbox has a different behavior; therefore, the first step to reach dynamic system condition monitoring is to
obtain reliable characteristic information from the system behavior. The next section goes into more detail regarding potential
fault diagnosis schemes.
3.2 Remaining useful life
The prediction of the consumed life of existing installations of mechanical components of the drivetrain, such as bearings and5
gears, requires the analysis of measurements from the field, such as gearbox temperatures, bearing vibration measurements,
and wind turbine operational measurements. Such measurements can be continuous online recordings or measurements taken
during inspections such as oil quality sampling. Typical indicators for the consumption of the life of the gearbox are:
The number of particles in the gearbox oil per time duration or the particulates in the grease for greased bearings (Feng
et al., 2013)10
The size of the particles in the oil
The frequency of the oil temperature or bearing temperature excursions above a threshold (Feng et al., 2013)
Changes in vibration spectrum signatures.
The distribution of particulates in the oil or grease can be analyzed in a laboratory to identify the chemicals present and thereby
their source to determine their origin from the bearings or gears.15
With advanced multibody simulation tools, it is also possible to model all elements of the drivetrain fully coupled to the
aeroelastic interactions of the rotor (Gallego-Calderon and Natarajan, 2015; Gallego-Calderon et al., 2017; Wang et al., 2020)
and mounted on a flexible tower. In such a software tool, the drivetrain is subject to continuous wind-driven excitation and
grid-driven events. If the response of the drivetrain in terms of the bearing displacements and shaft loads is validated with the
physical turbine during different operating conditions, then the software tool can be used to track fatigue damage consumption20
in the drivetrain by supplying it with measured operating conditions from the physical turbine. This also requires that specific
drivetrain component failure modes are tracked in the simulation, such as the occurrences of bearing roller sliding (Dabrowski
and Natarajan, 2017) or the vibration excitation of different components. Monitoring fatigue damage growth also provides
knowledge of the remaining useful life of the drivetrain if the design life of the components of the drivetrain are known.
Moreover, the inverse methods can also be employed to estimate the loads on drivetrain components from SCADA and25
vibration measurements and can be used to estimate the component fatigue damage and RUL (Mehlan et al., 2021). Dynamic
models of the drivetrain might be needed in some cases if not enough data or measurements are available that can be developed
provided that basic information about the drivetrain (e.g., geometry, bearing types, and gear teeth numbers) is known (van
Binsbergen et al., 2021).
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
4 Lifetime extension
Once a turbine reaches its 20-year design life, there are, in principle, three options that can be considered by the operator:
decommissioning, full repowering, or lifetime extension (Tartt et al., 2021). Full repowering and lifetime extension can accom-
plish the goal of extending the service life of existing wind turbines. The main benefit is to increase returns on investments and
reduce the levelized cost of energy for wind power. Full repowering refers to the dismantling of old wind turbines and replacing5
them with new ones. Lifetime extension, on the other hand, refers to the assessment of the remaining useful life and possible
turbine components upgrades while keeping the turbine hub height, size, or plant layout unchanged. Partial repowering or
refurbishment (Topham and McMillan, 2017) is another term used in the industry for replacing and upgrading the components;
this can be considered an option in the lifetime extension process.
Whether and how to conduct lifetime extension is a complex decision-making process and depends on many factors, in-10
cluding technical, economical, and legal (Ziegler et al., 2018). The prerequisite is that the turbine’s structural integrity—e.g.,
foundation, tower, nacelle, and hub—has been assessed and can be safely used throughout the expected turbine lifetime ex-
tension span. Sometimes these assessments also includes blades, which, if not strong enough, could be upgraded as well.
Specific to drivetrain components, typical practices are replacing them with newer products—e.g., gearboxes, main bear-
ings, or generators—which have improved performance and reliability. On the other hand, from the drivetrain research-and-15
development perspective, some opportunities lie in reliability assessment and driving events identification, which can benefit
from fault diagnostics and RUL prediction research conducted to support wind plant O&M. The expected outputs are more
accurate and reliable drivetrain component integrity assessment based on historical data or inspection as well as prediction
throughout the planned lifetime extension period. Should the evaluation be positive with an acceptable confidence level, the
drivetrain components might not be replaced, as is currently practiced now, and additional costs of the turbine lifetime extension20
can be reduced.
Tartt et al. (2021) investigated the lifetime extension practices in other industries and proposed a methodology for wind
turbine drivetrains. The industry’s experience with lifetime extension is still limited. Also, different markets—e.g., Europe or
the United States—might require different strategies. There are a few uncertainty concerns: i) technically, how trustworthy is
the integrity assessment of the structural components; ii) economically, how the assumed future electricity prices might hold25
true; and iii) legally, how related policies might change.
5 Decommissioning and recycling
Wind turbines are typically decommissioned and recycled—at least partially—at the end of their service life because of both
their salvage value and local legislative requirements, so decommissioning has increasingly become part of the planning process
(Jensen, 2019). Detailed discussions on the decommissioning process for land-based and offshore wind turbines, recycling30
analysis of different turbine subsystems, basic cost analysis, and the environmental impacts can be found in Jensen (2019). The
main challenges surrounding the decommissioning process of wind plants are the regulatory framework, the overall planning
of the process, the transportation logistics, and the environmental impacts (Topham et al., 2019a).
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Studies show that recycling after decommissioning can pay back some of the decommissioning costs (Topham et al., 2019b).
The main parts of the drivetrain from the recycling perspective are the bearings, gears, frames, shafts, couplings, windings,
cores (generator stator and rotor), permanent magnets, hydraulic cooling systems, and electronics. Windings are made of
copper. Cores are made of electrical steel lamination, which is an iron alloy to which silicon is added. Bearings, gears, frames,
shafts, and couplings are made of different types of alloy steel. Permanent magnets used in wind turbine PMSGs are often5
NdFeB magnets. Hydraulics are considered less problematic because the recycling industry is accustomed to handling the
related components. Reuse of lubricants is also being explored (SKF, 2021). Electronics are usually difficult to recycle because
of their complex material composition. Nearly all waste electronics equipment is currently shredded, where further physical
processes are applied, including magnetic and electrostatic techniques to separate different metal fractions. Two main methods
are used to recycle these components: shredding and disassembling. The method used depends on the recycled component,10
the size, and the material. Recycling can be supported by robotics and using optimization models to optimize the process
(Rassõlkin et al., 2018). Separate parts of disassembled components are reused if they do not have any damage. The rest are
remelted as the same raw material or to a new alloy. The recycling cost analysis of the drivetrain at the end of the life cycle can
be performed by using the flowchart shown in Figure 6. The overall recycling process should be economical under the current
conditions and raw material prices.15
Collection of drivetrain components
Transportation to factory
Disassempling and shredding
Separating parts for reusing or remelting
Extraction of different parts or materials
Figure 6. Recycling cost analysis of the drivetrain at end of life.
High-volume drivetrain recycling needs to minimize environmental impact (no harmful chemicals/emissions or energy-
intensive processes) and have high recovery rates of rare and precious materials while still being economically viable. Recycling
can also be in the design phase. For example, surface-mounted PMSGs compared to interior rotor magnets, have different
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
disassembling procedures. Modular design can also offer a clear advantage in recycling because healthy parts can be separately
reused in other components of that type.
Further, the use of recycled material can lead to cheaper new products (Gabhane and Kaddoura, 2017), which, apart from
being more environmentally friendly, can be a good motivation for recycling. Recycling and reusing can also contribute to
reduce the potential supply chain risk, especially for rare-earth materials (Habib and Wenzel, 2014).5
6 Emerging areas
6.1 Drivetrain in floating turbines
Drivetrains in floating wind turbines are exposed to different dynamic loads than those on bottom-fixed or land-based ones.
Apart from the wind loads, the wave-induced motions can affect the drivetrain load responses. The wave-induced motions can
have a negative impact on the main bearing fatigue life—particularly on the one carrying axial loads—as highlighted by Nejad10
et al. (2015). Sethuraman et al. (2014) investigated the effects of the floating wind turbine motion on direct-drive generator air
gap integrity and showed that the air gap stability of the generator is more sensitive to magnetic forces if the supporting frame
is relatively rigid. They also highlighted the need for air gap management for direct-drive generators on floating platforms.
A recent full-scale experimental field study of a 6-MW drivetrain on a spar floating substructure (Torsvik et al., 2021)
indicates that the effect of wave-induced motions might not be as significant as the wind loading on the drivetrain responses,15
particularly in larger turbines. A set of strain measurements on the main bearing shows that the effect of wave motions is
negligible compared with the tower shadow excitation (Torsvik et al., 2021).
The study by Nejad and Torsvik (2021) investigated the lessons learned in the last 10 years with regard to drivetrains
on floating wind turbines. Among others, the study highlighted that the maximum tower top axial acceleration might not
be a reasonable limiting factor for the fatigue life of main bearings in floating wind turbines (Nejad et al., 2019). It also20
emphasized that a flexible bedplate influences the main bearing and components inside the gearbox, and therefore the coupling
effects between the structure and the drivetrain need to be considered in floating wind turbines, particularly in compact design
concepts. Given the limited experience with floating wind turbines, however, more research is needed.
6.2 Drivetrain and plant consideration
Wakes induced by other turbines in the plant cause increased turbulence and thus can affect the loading on the drivetrain25
(Roscher et al., 2017). How the wakes are controlled at the plant level will therefore influence the drivetrain design life (van
Binsbergen et al., 2020). Wake steering typically does not have large effects on the drivetrain of the upstream turbine, whereas
an increase in the yaw angle would lead to an increase in the drivetrain load variation of the downstream turbine (van Binsbergen
et al., 2020). In contrast, an increase in the blade pitch angle would reduce the standard deviations of the drivetrain dynamic
response of the upstream wind turbine, whereas it would not significantly affect the standard deviations in the downstream30
turbine (van Binsbergen et al., 2020).
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Based on the study performed by van Binsbergen et al. (2020), the wake impact on the downstream turbine can reduce the
lifetime of the first main bearing of the drivetrain by 17%, and it can reduce the turbine power intake by 30%. The mitigation
of loads on the drivetrain of the wind turbine and an increase of power capture at the turbine level is addressed in the literature
on turbine control by optimizing the generator torque, blade pitch, and yaw steering controls (as shown in, e.g., van Binsbergen
et al. (2020) and Fleming et al. (2013)). Optimized wind power plant management by considering the influence of wakes was5
recently studied by Andersson and Imsland (2020).
The optimized plant control design to maximize the wind plant power intake and simultaneously minimize the degradation of
the drivetrain components influenced by wake loads is still an open research problem. The latter calls for high-fidelity models
of wake flow, wake loads on the drivetrain of downstream turbines and the drivetrain system to study the drivetrain load effects
and responses, and, finally, the derivation of sufficient drivetrain lifetime-related constraints that can be integrated into the10
plant stochastic model predictive control design framework. From this perspective, the additional role of wind plant control is
to distribute accumulated fatigue evenly over the drivetrains of different turbines to improve the reliability of the plant.
6.3 Drivetrain and digitalization
The use of digital technologies and digitized data can support wind turbine drivetrain analyses at both the system and com-
ponent levels during the life cycle—including design, installation, and O&M—and lifetime extension from various aspects,15
namely, by receiving and transmitting real-time data through the data acquisition and transmission layers; by storing and pro-
cessing data through the platform layer, and, finally, by decision support through the application layer (An et al., 2021). To this
purpose, digitalization targets the sensors and actuators installed on the drivetrain and the other turbine systems—such as the
site network, servers, and even smart phones (André et al., 2021)—that are connected to the turbine and plant’s control and
monitoring systems to improve reliability, availability, quality of service, and user experiences.20
Digitalization enables digital twin models that can support the drivetrain’s design and operation (Moghadam and Nejad,
2022). Digital twin in this context includes sensors (data collection), models (dynamic and degradation models), and decision
support platform (e.g., estimation of remaining useful life) (Johansen and Nejad, 2019). The application of computationally
inexpensive digital twin models for the predictive maintenance of drivetrain system components by monitoring the remaining
useful lifetime of the critical components (e.g., the gears of the gearbox) in real time has been proposed in recent literature25
(Moghadam et al., 2021). Using the existing physical models, integrating them in a unified framework, applying signal pro-
cessing techniques to estimate these models and model inputs in real time from available measurements, optimizing the data
streaming between models, using continuous processing architectures, and using statistical approaches and stochastic modeling
techniques to model and mitigate the impact of uncertainties are the tasks under the umbrella of a digital twin. As discussed by
(Moghadam et al., 2021), challenges of digital twin models mainly arise from:30
Finding the minimum or appropriate model fidelity required to capture the dynamics of the component for different
operation and failure modes for a wide range of drivetrain components
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Optimizing data streaming between models, data processing algorithms, and continuously processing architectures to
deal with the real-time aspects of digital twin models
Developing a preprocessing stage to model and mitigate the different sources of uncertainty and to verify and ensure
time synchronicity when the data come from different sources and when dealing with the different sample rates.
To overcome these challenges, merging edge computing with the Internet of things (IoT), as shown in Figure 7, can play5
a significant role. Using distributed computing algorithms supported by edge computing data handling architectures (e.g., fog
computing) has been recently adapted from computer science for wind plant control and monitoring systems to significantly
reduce the computational burden of implementing digital twin models’ complex algorithms to monitor drivetrain components
in future wind turbine O&M analyses (Verstraeten et al., 2019). By using fog computing, it is possible to break the digital
twin into simple subproblems with less computational complexity, and each one can be executed on a fog. IoT can provide10
real-time access to data in each network node with the possibility of using the processing and storage capacities of the nodes
for control and monitoring purposes, and not all data in fogs need to be shared with other fogs or even the cloud. Cybersecurity
frameworks should be deployed at the communication network and the computing modules to guarantee the integrity, authen-
ticity, confidentiality, and availability of the data while they are being transmitted over the network nodes. Because future wind
plants’ control and monitoring systems will need to handle more voluminous, heterogeneous data and distributed features, but15
storage and processing capacities are limited, the IoT can significantly improve turbine control and monitoring.
7 Summary and concluding remarks
This paper presented the state of the art and future development trends for wind turbine drivetrain technologies from the life-
cycle perspective. Lighter and more compact design concepts are the most cost-effective option, particularly for large offshore
turbines. As a result, the integration of electromechanical systems together with the main bearing has been a recent trend. For20
land-based applications, geared drivetrains seem to be the dominant technology; however, the race between geared or not is
still ongoing for offshore, with medium-speed (or hybrid concept) or even high-speed geared drivetrains under development
for large offshore wind turbines. The extensive research and development of gearbox and bearing design has mainly been
on improving reliability by better understanding the failure modes in operation, on one hand and, on the other hand, on the
development of new technologies, e.g., plain bearings. The medium-speed concepts have shown to be a promising compromise25
between high-speed and drive-drive designs, whereas high-speed, multistage gearboxes have also been a research focus in
recent years. For offshore turbine generators, there has been a recent increasing trend toward direct-drive systems, PMSGs
with full-power converter systems rather than DFIGs with partial-power converter systems. Superconducting generators are
also seen as an attractive alternative to PMSGs because of the large amount of rare-earth materials needed for PMSGs.
Given the criticality of drivetrain components in wind turbines, condition-based maintenance is seen as an essential ele-30
ment, at least for offshore and large turbines. In newer and larger turbines, greater than 2.5 MW or 3 MW, most if not all
have dedicated condition monitoring systems, which typically monitor the gearbox, main bearings, and generator. Their out-
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
I/O ...
Figure 7. Drivetrain system monitoring by means of digital twin models and distributed data management architectures and algorithms.
puts can be used to support anomaly detection, fault diagnostics, and prognostics models. These models can be data-driven,
physics-based, or hybrid, integrating both data and physics-domain models. In the data domain, particularly for SCADA-based
condition monitoring, machine learning and artificial intelligence technologies are being actively investigated for wind turbine
Condition monitoring technologies deployed for wind turbine drivetrains are generally good at fault diagnostics, especially5
for high-speed components. The performance of various solutions in terms of prognostics still needs to improve, which presents
an opportunity to bridge progress made by the research community. On the other hand, the industry has long been eager to
obtain accurate predictions of component remaining useful life, which is one objective of typical fault prognostics. Among
the various drivetrain subcomponents, bearing faults have shown to be prevalent and have been actively investigated by both
industry and researchers.10
As the industry moves farther offshore and into deeper water, increasing numbers of floating wind turbines are expected to
be used. The early lessons highlight differences in terms of dynamic behavior and life of the drivetrain in floating wind turbines
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
compared with fixed ones, especially for the main bearings. As offshore turbine sizes are increasing, the component flexibility
and potential dynamic coupling effects should not be overlooked during design modeling and analysis.
Another emerging area of research for drivetrains is digitalization. Apart from data processing and digital models, data
handling, ownership, security, communication, and transfer at the scale of large wind plants are also interesting challenges.
The use of digital twins for condition monitoring is also a recent research direction.5
This article also illustrated that the drivetrains in wind turbines are very multidisciplinary objects in all stages of their life
cycles—from design to operation, to lifetime extension, to end of service and recycling. This calls for more interdisciplinary
research and collaborations to improve wind turbine drivetrain reliability and availability with the main aim to reduce the cost
of energy over time.
Competing interests. No competing interests are present10
Acknowledgements. This paper has been prepared by the Drivetrain Technical Committee (DTC) at the European Academy of Wind Energy
(EAWE) over period of 2020-2021. The authors appreciate fruitful discussions in DTC and acknowledge EAWE for facilitating this forum
for the wind research community.
This work was also authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for
the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office15
of Energy Efficiency and Wind Energy Technologies Office. The views expressed herein do not necessarily represent the views of the DOE
or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S.
Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow
others to do so, for U.S. Government purposes.
E. Hart is funded by a Brunel Fellowship from the Royal Commission for the Exhibition of 1851.20
Pieter-Jan Daems, Timothy Verstraeten, Cédric Peeters, and Jan Helsen received funding from the Flemish Government (AI Research
Program). They would like to acknowledge FWO (Fonds Wetenschappelijk Onderzoek) for their support through the SB grant of Timothy
Verstraeten (#1S47617N) and post-doctoral grant of Cédric Peeters (#1282221N). They would also like to acknowledge VLAIO for the
support through the SIM MaDurOS program project SBO MaSiWEC (H.B.C.2017.0606) and Blauwe Cluster ICON project Supersized 4.0.
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
ABB: PCS6000 Medium voltage wind turbine converter,
ABB: Generators for wind turbines - Generators | ABB,,
Abboud, D., Antoni, J., Sieg-Zieba, S., and Eltabach, M.: Deterministic-random separation in nonstationary regime, Journal of Sound and
Vibration, 362, 305–326, 2016.
Abboud, D., Antoni, J., Sieg-Zieba, S., and Eltabach, M.: Envelope analysis of rotating machine vibrations in variable speed conditions: A
comprehensive treatment, Mechanical Systems and Signal Processing, 84, 200–226, 2017.
Ali, J. B., Saidi, L., Harrath, S., Bechhoefer, E., and Benbouzid, M.: Online automatic diagnosis of wind turbine bearings progressive10
degradations under real experimental conditions based on unsupervised machine learning, Applied Acoustics, 132, 167–181, 2018.
An, J., Zou, Z., Chen, G., Sun, Y., Liu, R., and Zheng, L.: An IoT-Based Life Cycle Assessment Platform of Wind Turbines, Sensors, 21,
1233, 2021.
Andersson, A. and Vedmar, L.: A dynamic model to determine vibrations in involute helical gears, Journal of Sound and Vibration, 260,
195–212, 2003.15
Andersson, L. E. and Imsland, L.: Real-time optimization of wind farms using modifier adaptation and machine learning, Wind Energy
Science, 5, 885–896, 2020.
André, H., Leclere, Q., Anastasio, D., Benaïcha, Y., Billon, K., Birem, M., Bonnardot, F., Chin, Z., Combet, F., Daems, P., et al.: Using
a smartphone camera to analyse rotating and vibrating systems: Feedback on the SURVISHNO 2019 contest, Mechanical Systems and
Signal Processing, 154, 107 553, 2021.20
Antoni, J. and Randall, R.: Unsupervised noise cancellation for vibration signals: part I evaluation of adaptive algorithms, Mechanical
Systems and Signal Processing, 18, 89–101, 2004a.
Antoni, J. and Randall, R.: Unsupervised noise cancellation for vibration signals: part II a novel frequency domain algorithm, Mechanical
Systems and Signal Processing, 18, 103–117, 2004b.
Arabian-Hoseynabadi, H., Tavner, P., and Oraee, H.: Reliability comparison of direct-drive and geared-drive wind turbine concepts, Wind25
Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology, 13, 62–73, 2010.
Ashuri, T., Martins, J. R., Zaaijer, M. B., van Kuik, G. A., and van Bussel, G. J.: Aeroservoelastic design definition of a 20 MW common
research wind turbine model, Wind Energy, 19, 2071–2087, 2016.
Bedrosian, E.: A product theorem for Hilbert transforms, Proceedings of the IEEE, 51, 868–869, 1963.
Bergen, A., Andersen, R., Bauer, M., Boy, H., Brake, M. T., Brutsaert, P., Bührer, C., Dhallé, M., Hansen, J., ten Kate, H., Kellers, J., Krause,30
J., Krooshoop, E., Kruse, C., Kylling, H., Pilas, M., Pütz, H., Rebsdorf, A., Reckhard, M., Seitz, E., Springer, H., Song, X., Tzabar, N.,
Wessel, S., Wiezoreck, J., Winkler, T., and Yagotyntsev, K.: Design and in-field testing of the world’s first ReBCO rotor for a 3.6 MW
wind generator, Superconductor Science and Technology, 32, 125006+12, 2019.
Blaabjerg, F. and Ma, K.: Future on power electronics for wind turbine systems, IEEE Journal of emerging and selected topics in power
electronics, 1, 139–152, 2013.35
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Blockmans, B., Helsen, J., Vanhollebeke, F., and Desmet, W.: Dynamic response of a multi-megawatt wind turbine drivetrain under volt-
age dips using a coupled flexible multibody approach, in: International Design Engineering Technical Conferences and Computers and
Information in Engineering Conference, vol. 55928, p. V005T11A045, American Society of Mechanical Engineers, 2013.
Blockmans, B., Tamarozzi, T., Naets, F., and Desmet, W.: A nonlinear parametric model reduction method for efficient gear contact simula-
tions, International Journal for Numerical Methods in Engineering, 102, 1162–1191, 2015.5
Bosmans, J., Vanommeslaeghe, Y., Geens, L., Fiszer, J., Croes, J., Kirchner, M., Denil, J., De Meulenaere, P., and Desmet, W.: Development
and embedded deployment of a virtual load sensor for wind turbine gearboxes, in: Journal of Physics: Conference Series, vol. 1618, p.
022011, IOP Publishing, 2020.
Bozchalooi, I. S. and Liang, M.: A smoothness index-guided approach to wavelet parameter selection in signal de-noising and fault detection,
Journal of Sound and Vibration, 308, 246–267, 2007.10
Breteler, D., Kaidis, C., Tinga, T., and Loendersloot, R.: Physics based methodology for wind turbine failure detection, diagnostics &
prognostics, EWEA 2015 Annual Event, 2015.
Bruce, T., Long, H., and Dwyer-Joyce, R. S.: Dynamic modelling of wind turbine gearbox bearing loading during transient events, IET
Renewable Power Generation, 9, 821–830, 2015.
Cai, Y. and Hayashi, T.: The linear approximated equation of vibration of a pair of spur gears (theory and experiment), Journal of Mechanical15
Design, 116, 558–564, 1994.
Carroll, J.: Cost of energy modelling and reduction opportunities for offshore wind turbines, Ph.D. thesis, University of Strathclyde, 2016.
Carroll, J., McDonald, A., and McMillan, D.: Reliability comparison of wind turbines with DFIG and PMG drive trains, IEEE Transactions
on Energy Conversion, 30, 663–670, 2014.
Carroll, J., McDonald, A., Dinwoodie, I., McMillan, D., Revie, M., and Lazakis, I.: Availability, operation and maintenance costs of offshore20
wind turbines with different drive train configurations, Wind Energy, 20, 361–378, 2017.
Catmull, S.: Self-organising map based condition monitoring of wind turbines, in: EWEA Annual Conf, vol. 2011, 2011.
Chen, B., Tavner, P. J., Feng, Y., Song, W. W., and Qiu, Y.: Bayesian network for wind turbine fault diagnosis., 2012.
Chen, X., Xu, W., Liu, Y., and Islam, M. R.: Bearing Corrosion Failure Diagnosis of Doubly Fed Induction Generator in Wind Turbines Based
on Stator Current Analysis, IEEE Transactions on Industrial Electronics, 67, 3419–3430,, 2020.25
Cornel, D., Guzmán, F. G., Jacobs, G., and Neumann, S.: Acoustic response of roller bearings under critical operating conditions, Tech. rep.,
World Congress on Engineering Asset Management. Stavanger, 2018.
Craig Jr, R. R. and Ni, Z.: Component mode synthesis for model order reduction of nonclassicallydamped systems, Journal of Guidance,
Control, and Dynamics, 12, 577–584, 1989.
Cross, P. and Ma, X.: Model-based and fuzzy logic approaches to condition monitoring of operational wind turbines, International Journal30
of Automation and Computing, 12, 25–34, 2015.
Dabrowski, D. and Natarajan, A.: Identification of loading conditions resulting in roller slippage in gearbox bearings of large wind turbines,
wind Energy, 20, 1365–1387, 2017.
Daners, D. and Nickel, V.: More torque is better than torque: Higher torque density for gearboxes, in: Conference for Wind Power Drives
2021: Conference Proceedings, 2021.35
de Azevedo, H. D. M., Araújo, A. M., and Bouchonneau, N.: A review of wind turbine bearing condition monitoring: State of the art and
challenges, Renewable and Sustainable Energy Reviews, 56, 368–379, 2016.
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
De Mul, J., Vree, J., and Maas, D.: Equilibrium and associated load distribution in ball and roller bearings loaded in five degrees of freedom
while neglecting friction—Part I: general theory and application to ball bearings, Journal of Tribology, 111, 142–148, 1989a.
De Mul, J., Vree, J., and Maas, D.: Equilibrium and associated load distribution in ball and roller bearings loaded in five degrees of freedom
while neglecting friction—Part II: application to roller bearings and experimental verification, Journal of Tribology, 111, 149–155, 1989b.
Decker, H. J., Handschuh, R. F., and Zakrajsek, J. J.: An enhancement to the NA4 gear vibration diagnostic parameter, 1994.5
D’Elia, G., Cocconcelli, M., Rubini, R., and Dalpiaz, G.: Evolution of gear condition indicators for diagnostics of planetary gearboxes, in:
The International Conference Surveillance 8, FRA, 2015.
Demtröder, J., Kjaer, P., and Hansen, A.: Balancing Incremental Development and Disruptive Innovation in the Design of a Modularized,
Scalable Powertrain for the Modular Windturbine Product System EnVentus, in: Dresdner Maschi-nenelemente Kolloquium, 2019.
Dhanola, A. and Garg, H.: Tribological challenges and advancements in wind turbine bearings: A review, Engineering Failure Analysis, 118,10
1861–1863, 2020.
DHHI: 1.5MW Planetary Wind Turbine Gearbox,
Ding, F., Tian, Z., Zhao, F., and Xu, H.: An integrated approach for wind turbine gearbox fatigue life prediction considering instantaneously
varying load conditions, Renewable energy, 129, 260–270, 2018.
DOE: Wind Vision: A New Era for Wind Power in the United States, Tech. Rep. DOE/GO-102015-4557, U.S. Department of Energy, 2015.15
Dong, W., Nejad, A. R., Moan, T., and Gao, Z.: Structural reliability analysis of contact fatigue design of gears in wind turbine drivetrains,
Journal of Loss Prevention in the Process Industries, 65, 104115, 2020.
Dragomiretskiy, K. and Zosso, D.: Variational mode decomposition, IEEE transactions on signal processing, 62, 531–544, 2013.
Equinor: Statoil completes Dogger Bank transaction,,
[Online; accessed March 23, 2017].20
Erdman, W., Keller, J., Grider, D., and VanBrunt, E.: A 2.3-MW Medium-Voltage, Three-Level Wind Energy Inverter Applying a Unique
Bus Structure and 4.5-kV Si/SiC Hybrid Isolated Power Modules, in: 2015 IEEE Applied Power Electronics Conference, 2015.
Eritenel, T. and Parker, R. G.: Three-dimensional nonlinear vibration of gear pairs, Journal of sound and vibration, 331, 3628–3648, 2012.
EU, 2019: Onshore and offshore wind,, [Online; ac-
cessed 14-August-2020], a.25
EU, 2019: A European Green Deal,, [Online; accessed 14-
August-2020], b.
Feng, M., Ma, H., Li, Z., Wang, Q., and Wen, B.: An improved analytical method for calculating time-varying mesh stiffness of helical gears,
Meccanica, 53, 1131–1145, 2018.
Feng, Y., Qiu, Y., Crabtree, C., Long, H., and Tavner, P.: Monitoring wind turbine gearboxes, wind Energy, 16, 728–740, 2013.30
Fiszer, J., Tamarozzi, T., and Desmet, W.: A semi-analytic strategy for the system-level modelling of flexibly supported ball bearings,
Meccanica, 51, 1503–1532, 2016.
Fleming, P. A., Van Wingerden, J.-W., Scholbrock, A. K., Van der Veen, G., and Wright, A. D.: Field testing a wind turbine drivetrain/tower
damper using advanced design and validation techniques, in: 2013 American Control Conference, pp. 2227–2234, IEEE, 2013.
Frank, M., Frauenhofer, J., van Hasselt, P., Nick, W., Neumueller, H., and Nerowski, G.: Long-term operational experience with first Siemens35
400 kW HTS machine in diverse configurations, IEEE Transactions on Applied Superconductivity, 13, 2120–2123, 2003.
Fu, L., Wei, Y., Fang, S., Zhou, X., and Lou, J.: Condition monitoring for roller bearings of wind turbines based on health evaluation under
variable operating states, Energies, 10, 1564, 2017.
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Gabhane, P. and Kaddoura, M.: Remanufacturing in Circular Economy-A Gearbox Example, 2017.
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G. E., Abbas, N. J., Meng, F., Bortolotti, P., Skrzypinski, W., et al.:
IEA wind TCP task 37: Definition of the IEA 15-megawatt offshore reference wind turbine, Tech. rep., National Renewable Energy
Lab.(NREL), Golden, CO (United States), 2020.
Gallego-Calderon, J. and Natarajan, A.: Assessment of wind turbine drive-train fatigue loads under torsional excitation, Engineering Struc-5
tures, 103, 189–202, 2015.
Gallego-Calderon, J., Natarajan, A., and Cutululis, N. A.: Ultimate design load analysis of planetary gearbox bearings under extreme events,
Wind Energy, 20, 325–343, 2017.
Garcia, M. C., Sanz-Bobi, M. A., and Del Pico, J.: SIMAP: Intelligent System for Predictive Maintenance: Application to the health condition
monitoring of a windturbine gearbox, Computers in Industry, 57, 552–568, 2006.10
Garlick, W. G., Dixon, R., and Watson, S. J.: A model-based approach to wind turbine condition monitoring using SCADA data, ICSE, 2009.
Godwin, J. L. and Matthews, P.: Classification and detection of wind turbine pitch faults through SCADA data analysis, IJPHM Special Issue
on Wind Turbine PHM, p. 90, 2013.
Gray, C. S. and Watson, S. J.: Physics of failure approach to wind turbine condition based maintenance, Wind Energy, 13, 395–405, 2010.
Guo, Y. and Parker, R. G.: Stiffness matrix calculation of rolling element bearings using a finite element/contact mechanics model, Mecha-15
nism and machine theory, 51, 32–45, 2012.
Guo, Y., Bergua, R., van Dam, J., Jove, J., and Campbell, J.: Improving wind turbine drivetrain reliability using a combined experimental,
computational, and analytical approach, in: International Design Engineering Technical Conferences and Computers and Information in
Engineering Conference, vol. 46407, p. V007T05A004, American Society of Mechanical Engineers, 2014.
Guo, Y., Lambert, S., Wallen, R., Errichello, R., and Keller, J.: Theoretical and experimental study on gear-coupling contact and loads20
considering misalignment, torque, and friction influences, Mechanism and Machine Theory, 98, 242–262, 2016.
Habib, K. and Wenzel, H.: Exploring rare earths supply constraints for the emerging clean energy technologies and the role of recycling,
Journal of Cleaner Production, 84, 348–359, 2014.
Hart, E.: Developing a systematic approach to the analysis of time-varying main bearing loads for wind turbines, Wind Energy, 23, 2150–
2165, 2020.25
Hart, E., Turnbull, A., Feuchtwang, J., McMillan, D., Golysheva, E., and Elliott, R.: Wind turbine main-bearing loading and wind field
characteristics, Wind Energy, 22, 1534–1547, 2019.
Hart, E., Clarke, B., Nicholas, G., Kazemi Amiri, A., Stirling, J., Carroll, J., Dwyer-Joyce, R., McDonald, A., and Long, H.: A review of
wind turbine main bearings: design, operation, modelling, damage mechanisms and fault detection, Wind Energy Science, 5, 105–124,,, 2020.30
Harzendorf, F.: Geared vs. direct drive – a holistic system comparison, in: Conference for Wind Power Drives 2021: Conference Proceedings,
Harzendorf, F., Schelenz, R., and Jacobs, G.: Reducing cost uncertainty in the drivetrain design decision with a focus on the operational
phase, Wind Energy Science, 6, 571–584, 2021.
He, G., Ding, K., Wu, X., and Yang, X.: Dynamics modeling and vibration modulation signal analysis of wind turbine planetary gearbox35
with a floating sun gear, Renewable Energy, 139, 718–729, 2019.
Helsen, J.: Review of Research on Condition Monitoring for Improved O&M of Offshore Wind Turbine Drivetrains, Acoustics Australia, pp.
1–8, 2021.
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Helsen, J., Vanhollebeke, F., Marrant, B., Vandepitte, D., and Desmet, W.: Multibody modelling of varying complexity for modal behaviour
analysis of wind turbine gearboxes, Renewable Energy, 36, 3098–3113, 2011.
Helsen, J., Vanhollebeke, F., Vandepitte, D., and Desmet, W.: Some trends and challenges in wind turbine upscaling, in: Proceedings of
ISMA International Conference On Noise And Vibration 2012, vol. 6, pp. 4345–4359, KATHOLIEKE UNIV LEUVEN, DEPT WERK-
Helsen, J., Peeters, C., Verstraeten, T., Verbeke, J., Gioia, N., and Nowé, A.: Fleet-wide condition monitoring combining vibration signal
processing and machine learning rolled out in a cloud-computing environment, in: International Conference on Noise and Vibration
Engineering (ISMA), 2018.
Ho, D. and Randall, R.: Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals, Mechanical systems
and signal processing, 14, 763–788, 2000.10
Hoang, T.-K., Quéval, L., Berriaud, C., and Vido, L.: Design of a 20-MW Fully Superconducting Wind Turbine Generator to Minimize the
Levelized Cos of Energy, IEEE Transactions on Applied Superconductivity, 28, 1–5,, 2018.
Hu, Y., Talbot, D., and Kahraman, A.: A Gear Load Distribution Model for a Planetary Gear Set With a Flexible Ring Gear Having External
Splines, Journal of Mechanical Design, 141, 2019.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., and Liu, H. H.: The empirical mode15
decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London.
Series A: mathematical, physical and engineering sciences, 454, 903–995, 1998.
Jensen, J. P.: Evaluating the environmental impacts of recycling wind turbines, Wind Energy, 22, 316–326, 2019.
Jensen, O. L., Heuser, L., and Petersen, K. E.: Prevention of “white etching cracks” in rolling bearings in Vestas wind turbines, in: Conference
for Wind Power Drives 2021: Conference Proceedings, 2021.20
Johansen, S. S. and Nejad, A. R.: On digital twin condition monitoring approach for drivetrains in marine applications, in: ASME 2019
38th International Conference on Ocean, Offshore and Arctic Engineering, American Society of Mechanical Engineers Digital Collection,
Johnson, K. L. and Johnson, K. L.: Contact mechanics, Cambridge university press, 1987.
Kahn-Jetter and, Z. L. and Wright, S.: Finite element analysis of an involute spline, J. Mech. Des., 122, 239–244, 2000.25
Kim, K., Parthasarathy, G., Uluyol, O., Foslien, W., Sheng, S., and Fleming, P.: Use of SCADA data for failure detection in wind turbines,
Tech. rep., National Renewable Energy Lab.(NREL), Golden, CO (United States), 2011.
Koukoura, S., Carroll, J., and McDonald, A.: A Diagnostic Framework for Wind Turbine Gearboxes Using Machine Learning, in: Annual
Conference of the PHM Society, vol. 11, 2019.
Kuang, J. and Yang, Y.: An estimate of mesh stiffness and load sharing ratio of a spur gear pair, Advancing power transmission into the 21 st30
century, pp. 1–9, 1992.
Kusiak, A. and Li, W.: The prediction and diagnosis of wind turbine faults, Renewable energy, 36, 16–23, 2011.
Kusiak, A. and Zhang, Z.: Analysis of wind turbine vibrations based on SCADA data, Journal of Solar Energy Engineering, 132, 031008,
Kusiak, A., Zheng, H., and Song, Z.: Models for monitoring wind farm power, Renewable Energy, 34, 583–590, 2009.35
Larumbe, L. B., Qin, Z., and Bauer, P.: Introduction to the analysis of harmonics and resonances in large offshore wind power plants, in:
2018 IEEE 18th International Power Electronics and Motion Control Conference (PEMC), pp. 393–400, IEEE, 2018.
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Larumbe, L. B., Qin, Z., and Bauer, P.: Output impedance modelling and sensitivity study of grid-feeding inverters with dual current control,
in: IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society, vol. 1, pp. 4007–4012, IEEE, 2019.
Leclère, Q., André, H., and Antoni, J.: A multi-order probabilistic approach for Instantaneous Angular Speed tracking debriefing of the
CMMNO14 diagnosis contest, Mechanical Systems and Signal Processing, 81, 375–386, 2016.
Li, H., Hu, Y., Yang, C., Chen, Z., Ji, H., and Zhao, B.: An improved fuzzy synthetic condition assessment of a wind turbine generator5
system, International Journal of Electrical Power & Energy Systems, 45, 468–476, 2013.
Li, J., Lei, X., Li, H., and Ran, L.: Normal behavior models for the condition assessment of wind turbine generator systems, Electric Power
Components and Systems, 42, 1201–1212, 2014.
Li, S. and Kahraman, A.: A tribo-dynamic model of a spur gear pair, Journal of Sound and Vibration, 332, 4963–4978, 2013.
Lim, T. C. and Singh, R.: Vibration transmission through rolling element bearings, part I: bearing stiffness formulation, Journal of sound and10
vibration, 139, 179–199, 1990.
Loriemi, A., Jacobs, G., Reisch, S., Bosse, D., and Schröder, T.: Experimental and simulation-based analysis of asymmetrical spherical roller
bearings as main bearings for wind turbines, Forschung im Ingenieurwesen, pp. 1–9, 2021.
Lu, B., Li, Y., Wu, X., and Yang, Z.: A review of recent advances in wind turbine condition monitoring and fault diagnosis, in: Power
Electronics and Machines in Wind Applications, 2009. PEMWA 2009. IEEE, pp. 1–7, IEEE, 2009.15
Lydia, M., Kumar, S. S., Selvakumar, A. I., and Kumar, G. E. P.: A comprehensive review on wind turbine power curve modeling techniques,
Renewable and Sustainable Energy Reviews, 30, 452–460, 2014.
Marrant, B., Vanhollebeke, F., and Peeters, J.: Comparison of multibody simulations and measurements of wind turbine gearboxes at Hansen’s
13 MW test facility, in: European Wind Energy Conference and Exhibition (EWEC), Date: 2010/04/20-2010/04/23, 2010.
McDonald, A. and Bhuiyan, N. A.: On the optimization of generators for offshore direct drive wind turbines, IEEE Transactions on Energy20
Conversion, 32, 348–358, 2016.
McDonald, A. and Jimmy, G.: Parallel wind turbine powertrains and their design for high availability, IEEE Transactions on Sustainable
Energy, 8, 880–890, 2016.
Mehlan, F. C., Nejad, A. R., and Gao, Z.: Estimation of wind turbine gearbox loads for online fatigue monitoring using inverse methods, in:
Proceedings of the ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering OMAE 2021, pp. OMAE2021–25
62 181, ASME, 2021.
Moghadam, F. K. and Nejad, A. R.: Evaluation of PMSG-based drivetrain technologies for 10-MW floating offshore wind turbines: Pros and
cons in a life cycle perspective, Wind Energy, 23, 1542–1563, 2020.
Moghadam, F. K. and Nejad, A. R.: Theoretical and experimental study of wind turbine drivetrain fault diagnosis by using torsional vibrations
and modal estimation, Journal of Sound and Vibration, p. 116223, 2021.30
Moghadam, F. K. and Nejad, A. R.: Online condition monitoring of floating wind turbines drivetrain by means of digital twin, Mechanical
Systems and Signal Processing, 162, 108 087, 2022.
Moghadam, F. K., Ebrahimi, S., Oraee, A., and Velni, J. M.: Vector control optimization of DFIGs under unbalanced conditions, International
Transactions on Electrical Energy Systems, 28, e2583, 2018.
Moghadam, F. K., Rebouças, G. F. d. S., and Nejad, A. R.: Digital twin modeling for predictive maintenance of gearboxes in floating offshore35
wind turbine drivetrains, Forschung im Ingenieurwesen, 85, 273–286, 2021.
Moore, S.: U.S. Seeks Superconducting Offshore Wind Generators,
us-seeks-superconducting-offshore-wind-generators, 2020.
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Morales-Espejel, G. and Gabelli, A.: A major step forward in life modeling, Power Transmission Engineering, 11, 36–40, 2017.
Nejad, A. R. and Torsvik, J.: Drivetrains on floating offshore wind turbines: lessons learned over the last 10 years, Forschung im Ingenieur-
wesen, 85, 335–343, 2021.
Nejad, A. R., Gao, Z., and Moan, T.: On long-term fatigue damage and reliability analysis of gears under wind loads in offshore wind turbine
drivetrains, International Journal of Fatigue, 61, 116–128, 2014a.5
Nejad, A. R., Odgaard, P. F., Gao, Z., and Moan, T.: A prognostic method for fault detection in wind turbine drivetrains, Engineering Failure
Analysis, 42, 324–336, 2014b.
Nejad, A. R., Bachynski, E. E., Kvittem, M. I., Luan, C., Gao, Z., and Moan, T.: Stochastic dynamic load effect and fatigue damage analysis
of drivetrains in land-based and TLP, spar and semi-submersible floating wind turbines, Marine Structures, 42, 137–153, 2015.
Nejad, A. R., Odgaard, P. F., and Moan, T.: Conceptual study of a gearbox fault detection method applied on a 5-MW spar-type floating wind10
turbine, Wind Energy, 21, 1064–1075, 2018.
Nejad, A. R., Bachynski, E. E., and Moan, T.: Effect of axial acceleration on drivetrain responses in a spar-type floating wind turbine, Journal
of Offshore Mechanics and Arctic Engineering, 141, 2019.
OpenPR: Direct Drive Wind Turbine Market Growth Analysis By Top Leading Players -Goldwind, Enercon, Siemens,
GE Energy, EWT, Lagerwey Wind, Leitwind, United Energies MTOI,
html, [Online; accessed 09-17-2018].
Palermo, A., Mundo, D., Hadjit, R., and Desmet, W.: Multibody element for spur and helical gear meshing based on detailed three-
dimensional contact calculations, Mechanism and machine theory, 62, 13–30, 2013.
Peeters, C., Guillaume, P., and Helsen, J.: Vibration-based bearing fault detection for operations and maintenance cost reduction in wind20
energy, Renewable Energy, 116, 74–87, 2018.
Peeters, C., Leclere, Q., Antoni, J., Lindahl, P., Donnal, J., Leeb, S., and Helsen, J.: Review and comparison of tacholess instantaneous speed
estimation methods on experimental vibration data, Mechanical Systems and Signal Processing, 129, 407–436, 2019.
Polikarpova, M. et al.: Liquid cooling solutions for rotating permanent magnet synchronous machines, 2014.
Polinder, H., Van der Pijl, F. F., De Vilder, G.-J., and Tavner, P. J.: Comparison of direct-drive and geared generator concepts for wind25
turbines, IEEE Transactions on energy conversion, 21, 725–733, 2006.
Polinder, H., Ferreira, J., Jensen, B., Abrahamsen, A., Atallah, K., and McMahon, R.: Trends in Wind Turbine Generator Systems, IEEE
Journal of Emerging and Selected Topics in Power Electronics, 1, 174–185,, 2013.
Qiao, W. and Qu, L.: Prognostic condition monitoring for wind turbine drivetrains via generator current analysis, Chinese Journal of Electrical
Engineering, 4, 80–89,, 2018.30
Qin, Z., Liserre, M., Blaabjerg, F., and Wang, H.: Energy storage system by means of improved thermal performance of a 3 mw grid side
wind power converter, in: IECON 2013-39th Annual Conference of the IEEE Industrial Electronics Society, pp. 736–742, IEEE, 2013.
Qin, Z., Blaabjerg, F., and Loh, P. C.: A rotating speed controller design method for power leveling by means of inertia energy in wind power
systems, IEEE Transactions on Energy Conversion, 30, 1052–1060, 2015a.
Qin, Z., Wang, H., Blaabjerg, F., and Loh, P. C.: The feasibility study on thermal loading control of wind power converters with a flexible35
switching frequency, in: 2015 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 485–491, IEEE, 2015b.
Qiu, Y., Feng, Y., Tavner, P., Richardson, P., Erdos, G., and Chen, B.: Wind turbine SCADA alarm analysis for improving reliability, Wind
Energy, 15, 951–966, 2012.
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Rai, A. and Upadhyay, S.: A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings, Tribology
International, 96, 289–306, 2016.
Randall, R. B.: Vibration-based condition monitoring: industrial, aerospace and automotive applications, John Wiley & Sons, 2011.
Rassõlkin, A., Kallaste, A., Orlova, S., Gevorkov, L., Vaimann, T., and Belahcen, A.: Re-use and recycling of different electrical machines,
Latvian Journal of Physics and Technical Sciences, 55, 13–23, 2018.5
Reisch, S.: Elastic interaction of the gearbox in powertrain concepts with increased integration level, in: Conference for Wind Power Drives
2021: Conference Proceedings, 2021.
Rolink, A., Schröder, T., Jacobs, G., Bosse, D., Hölzl, J., and Bergmann, P.: Feasibility study for the use of hydrodynamic plain bearings
with balancing support characteristics as main bearing in wind turbines, in: Journal of Physics: Conference Series, vol. 1618, p. 052002,
IOP Publishing, 2020.10
Rolink, A., Jacobs, G., Schröder, T., Keller, D., Jakobs, T., Bosse, D., Lang, J., and Knoll, G.: Methodology for the systematic design of
conical plain bearings for use as main bearings in wind turbines, Forschung im Ingenieurwesen, pp. 1–9, 2021.
Roscher, B., Werkmeister, A., Jacobs, G., and Schelenz, R.: Modelling of Wind Turbine Loads nearby a Wind Farm, in: Journal of Physics:
Conference Series, vol. 854, p. 012038, IOP Publishing, 2017.
Sartori, L., Bellini, F., Croce, A., and Bottasso, C.: Preliminary design and optimization of a 20MW reference wind turbine, in: Journal of15
Physics: Conference Series, vol. 1037, p. 042003, IOP Publishing, 2018.
Sawalhi, N. and Randall, R. B.: The application of spectral kurtosis to bearing diagnostics, in: Proceedings of ACOUSTICS, pp. 3–5, 2004.
Schlechtingen, M., Santos, I. F., and Achiche, S.: Wind turbine condition monitoring based on SCADA data using normal behavior models.
Part 1: System description, Applied Soft Computing, 13, 259–270, 2013.
Sethuraman, L., Venugopal, V., Zavvos, A., and Mueller, M.: Structural integrity of a direct-drive generator for a floating wind turbine,20
Renewable energy, 63, 597–616, 2014.
Sharma, V. and Parey, A.: A review of gear fault diagnosis using various condition indicators, Procedia Engineering, 144, 253–263, 2016.
Sheng, S.: Wind turbine gearbox condition monitoring round robin study-vibration analysis, Tech. rep., National Renewable Energy
Lab.(NREL), Golden, CO (United States), 2012.
Shipurkar, U., Ma, K., Polinder, H., Blaabjerg, F., and Ferreira, J. A.: A review of failure mechanisms in wind turbine gener-25
ator systems, in: 2015 17th European Conference on Power Electronics and Applications (EPE’15 ECCE-Europe), pp. 1–10,, 2015.
Shipurkar, U., Polinder, H., and Ferreira, J. A.: A review of methods to increase the availability of wind turbine generator systems, CPSS
Transactions on Power Electronics and Applications, 1, 66–82,,
uploads/soft/170214/1_1513557361.pdf, 2016.30
Shrestha, G., Polinder, H., Bang, D., and Ferreira, J. A.: Structural Flexibility: A Solution for Weight Reduction of Large Direct-Drive
Wind-Turbine Generators, IEEE Transactions on energy conversion, 25, 732–740, 2010.
Siemens: Wind Generators,, 2020.
Siemens Gamesa: Siemens Gamesa Renewable Energy is ready for the future,
Sieros, G., Chaviaropoulos, P., Sørensen, J. D., Bulder, B. H., and Jamieson, P.: Upscaling wind turbines: theoretical and practical as-
pects and their impact on the cost of energy: Upscaling wind turbines: theoretical and practical aspects, Wind Energ., 15, 3–17,,, 2012.
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Silva, P., Giuffrida, A., Fergnani, N., Macchi, E., Cantù, M., Suffredini, R., Schiavetti, M., and Gigliucci, G.: Performance prediction of a
multi-MW wind turbine adopting an advanced hydrostatic transmission, Energy, 64, 450–461, 2014.
SKF: Wind Generators,, 2021.
Smalley, J.: Turbine components: bearings, bearings-brakes-generators-hydraulics- seals-towers/,
[Online; accessed June 16, 2021], 2015.5
Spinato, F., Tavner, P. J., Van Bussel, G. J., and Koutoulakos, E.: Reliability of wind turbine subassemblies, IET Renewable Power Generation,
3, 387–401, 2009.
Stehouwer, E. and van Zinderen, G. J.: Conceptual nacelle designs of 10-20 MW wind turbines, Tech. Rep. Deliverable D3.41, 2016.
Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., Keane, J., and Nenadic, G.: Machine learning methods for wind
turbine condition monitoring: A review, Renewable energy, 133, 620–635, 2019.10
Struggl, S., Berbyuk, V., and Johansson, H.: Review on wind turbines with focus on drive train system dynamics, Wind Energy, 18, 567–590,
Sun, P., Li, J., Wang, C., and Lei, X.: A generalized model for wind turbine anomaly identification based on SCADA data, Applied Energy,
168, 550–567, 2016.
Tartt, K., Nejad, A. R., Amiri, A. K., and McDonald, A.: On lifetime extension of wind turbine drivetrains, in: Proceedings of the ASME15
2021 40th International Conference on Ocean, Offshore and Arctic Engineering OMAE 2021, pp. OMAE2021–62 516, ASME, 2021.
Tautz-Weinert, J. and Watson, S. J.: Using SCADA data for wind turbine condition monitoring–a review, IET Renewable Power Generation,
11, 382–394, 2016.
Tchakoua, P., Wamkeue, R., Ouhrouche, M., Slaoui-Hasnaoui, F., Tameghe, T. A., and Ekemb, G.: Wind turbine condition monitoring:
State-of-the-art review, new trends, and future challenges, Energies, 7, 2595–2630, 2014.20
Topham, E. and McMillan, D.: Sustainable decommissioning of an offshore wind farm, Renewable Energy, 102, 470–480,, 2017.
Topham, E., Gonzalez, E., McMillan, D., and João, E.: Challenges of decommissioning offshore wind farms: overview of the European
experience, in: Journal of Physics: Conference Series, vol. 1222, p. 012035, IOP Publishing, 2019a.
Topham, E., McMillan, D., Bradley, S., and Hart, E.: Recycling offshore wind farms at decommissioning stage, Energy policy, 129, 698–709,25
Torsvik, J., Nejad, A. R., and Pedersen, E.: Main bearings in large offshore wind turbines: development trends, design and analysis require-
ments, in: Journal of Physics: Conference Series, vol. 1037, p. 042020, IOP Publishing, 2018.
Torsvik, J., Nejad, A. R., and Pedersen, E.: Full-scale experimental field study of floater motion effects on a main bearing in a spar floating
wind turbine, Marine Structures, 2021.30
Tracht, K., Goch, G., Schuh, P., Sorg, M., and Westerkamp, J. F.: Failure probability prediction based on condition monitoring data of wind
energy systems for spare parts supply, CIRP Annals, 62, 127–130, 2013.
Turnbull, A., Carroll, J., McDonald, A., and Koukoura, S.: Prediction of wind turbine generator failure using two-stage cluster-classification
methodology, Wind Energy, 22, 1593–1602, 2019.
Vaes, D., Clement, P., and Lindstedt, U.: Roller bearings for the next generation of wind gearboxes, in: Conference for Wind Power Drives35
2021: Conference Proceedings, 2021.
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
van Binsbergen, D. W., Wang, S., and Nejad, A. R.: Effects of induction and wake steering control on power and drivetrain responses
for 10 MW floating wind turbines in a wind farm, Journal of Physics: Conference Series, 1618, 022 044,
6596/1618/2/022044,, 2020.
van Binsbergen, D. W., Nejad, A. R., and Helsen, J.: Dynamic model development of wind turbine drivetrains by using sensor measur-
ments, in: Proceedings of the ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering OMAE 2021, pp.5
OMAE2021–61 939, ASME, 2021.
Vanhollebeke, F., Peeters, J., Vandepitte, D., and Desmet, W.: Using transfer path analysis to assess the influence of bearings on structural
vibrations of a wind turbine gearbox, Wind Energy, 18, 797–810, 2015.
r, P., Kreidl, M., and Šmíd, R.: Condition indicators for gearbox condition monitoring systems, Acta Polytechnica, 45, 2005.
Veers, P., Sethuraman, L., and Keller, J.: Wind-power generator technology research aims to meet global-wind power ambitions, Joule, 4,10
1861–1863, 2020.
Verstraeten, T., Marulanda, F. G., Peeters, C., Daems, P.-J., Nowé, A., and Helsen, J.: Edge computing for advanced vibration signal process-
ing, in: Surveillance, Vishno and AVE conferences, 2019.
Vijayakar, S.: A combined surface integral and finite element solution for a three-dimensional contact problem, International Journal for
Numerical Methods in Engineering, 31, 525–545, 1991.15
Wächter, M., Lind, P., Hernandez, I. H., Rinn, P., Milan, P., Stoevesandt, B., and Peinke, J.: Efficient load and power monitoring by stochastic
methods, EWEA 2015 Annual Event, 2015.
Wang, Q., Zhao, B., Fu, Y., Kong, X., and Ma, H.: An improved time-varying mesh stiffness model for helical gear pairs considering axial
mesh force component, Mechanical Systems and Signal Processing, 106, 413–429, 2018.
Wang, S., Nejad, A. R., Bachynski, E. E., and Moan, T.: Effects of bedplate flexibility on drivetrain dynamics: Case study of a 10 MW spar20
type floating wind turbine, Renewable Energy, 161, 808–824, 2020.
Watson, S., Kennedy, I., and Gray, C.: The use of physics of failure modelling in wind turbine condition monitoring, in: EWEA Annual Conf,
vol. 2011, pp. 309–312, 2011.
Weber, A. and Hansen, A.: Focus areas in Vestas powertrain, in: Conference for Wind Power Drives 2021: Conference Proceedings, 2021.
Wilkinson, M., Darnell, B., Van Delft, T., and Harman, K.: Comparison of methods for wind turbine condition monitoring with SCADA25
data, IET Renewable Power Generation, 8, 390–397, 2014.
Wind Europe, 2020: Offshore Wind in Europe, Key trends and statistics 2019,
statistics/WindEurope-Annual-Offshore-Statistics-2019.pdf, [Online; accessed 14-August-2020].
Windpower, Z.: Modular Gearbox Platform Designs,, 2021.
Wiser, R., Bolinger, M., and Lantz, E.: Assessing Wind Power Operating Costs in the United States: Results from a Survey of Wind Industry30
Experts, Renewable Energy Focus, 30, 46–57, 2019.
Wu, Z. and Huang, N. E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method, Advances in adaptive data
analysis, 1, 1–41, 2009.
Yang, W., Court, R., and Jiang, J.: Wind turbine condition monitoring by the approach of SCADA data analysis, Renewable Energy, 53,
365–376, 2013.35
Yang, W., Tavner, P. J., Crabtree, C. J., Feng, Y., and Qiu, Y.: Wind turbine condition monitoring: technical and commercial challenges, Wind
Energy, 17, 673–693, 2014.
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
Yaramasu, V., Wu, B., Sen, P. C., Kouro, S., and Narimani, M.: High-power wind energy conversion systems: State-of-the-art and emerging
technologies, Proceedings of the IEEE, 103, 740–788,, 2015.
Zaher, A., McArthur, S., Infield, D., and Patel, Y.: Online wind turbine fault detection through automated SCADA data analysis, Wind
Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology, 12, 574–593, 2009.
Zakrajsek, J. J., Townsend, D. P., and Decker, H. J.: An analysis of gear fault detection methods as applied to pitting fatigue failure data,5
Zeichfüßl, R., Jöckel, A., Deicke, M., Daners, D., and Fox, C.: Integrated 3-stage planetary gearbox with oil-cooled generator, in: Conference
for Wind Power Drives 2021: Conference Proceedings, 2021.
Zheng, J., Ji, J., Yin, S., and Tong, V.-C.: Internal loads and contact pressure distributions on the main shaft bearing in a modern gearless
wind turbine, Tribology International, 141, 105960, 2020.10
Zhou, D., Blaabjerg, F., Lau, M., and Tonnes, M.: Thermal profile analysis of doubly-fed induction generator based wind power converter
with air and liquid cooling methods, in: 2013 15th European Conference on Power Electronics and Applications (EPE), pp. 1–10, IEEE,
Zhu, J., Nostrand, T., Spiegel, C., and Morton, B.: Survey of condition indicators for condition monitoring systems, in: Annu. Conf. Progn.
Heal. Manag. Soc, vol. 5, pp. 1–13, 2014.15
Ziegler, L., Gonzalez, E., Rubert, T., Smolka, U., and Melero, J. J.: Lifetime extension of onshore wind turbines: A re-
view covering Germany, Spain, Denmark, and the UK, Renewable and Sustainable Energy Reviews, 82, 1261–1271,,, 2018.
zu Braunschweig. Institut für Maschinenelemente, T. H. C.-W.: The deformation of loaded gears and the effect on their load-carrying capacity,
Department of Scientific & Industrial Research, 1951.20
Preprint. Discussion started: 24 June 2021
Author(s) 2021. CC BY 4.0 License.
... Developing a truly modular blade design for on-site assembly provides a clear solution to both the manufacturing challenge of multiple blade families, as well as enables the complex logistics of large blade transport. In a similar fashion, families of gearboxes are being developed that share common mechanical characteristics 1660 but can be configured relatively easily for different rotor diameters and wind resources (Nejad et al., 2021). In addition to on-site/port-side manufacturing of components, such as blades, gearboxes, generators, and nacelles, innovations in tower (Jay et al., 2016;GE, 2020), foundation, and installation technology that reduce logistics burdens and installation costs are occurring for both land-based and offshore wind. ...
... Thus, the industry and the research community have begun 1670 to look at alternatives, including processing methods to optimize the cost and performance of carbon fiber, as well as considering downwind turbine rotors to alleviate blade-tower clearance issues . Finally, the torque density of drivetrain components continues to increase as well, by either optimizing conventional designs with gearboxes or introducing new technologies such as superconducting generators (Nejad et al., 2021). ...
... Continued research into these failure modes that are most prevalent and costly is necessary. Innovations in material processing, coatings, lubricants, and additives also hold the possibility of addressing reliability challenges that have historically been experienced in the wind industry (Nejad et al., 2021). Moving toward damage-tolerant design will require a better understanding of how damage progresses from an initiation site to structural failure. ...
Full-text available
Wind energy is foundational for achieving 100 % renewable electricity production and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications, but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the boundaries of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges requiring innovation. Uncertainty in turbine wakes complicates both structural loading and energy production estimates and requires advances in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning. Finally, key recommended actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are summarized.
... Early detection of the anomalous behavior of rotating machinery has drawn significant attention in recent years since in large industrial applications the maintenance and downtime costs can add up to substantial amounts [1,2,3]. Furthermore, the complexity of rotating machinery has rapidly increased thanks to technological developments in recent years [4]. Accordingly, such machines comprise an immense amount of components which complicates keeping track of all kinematic information of every component. ...
... Except for geared variable-speed wind turbines, there are also directdrive (ungeared, fixed-speed) wind turbines without a speed-increasing gearbox [117], see Fig. 12. Direct-drive systems for wind turbines are potentially a more reliable alternative to gearbox-driven systems, which can eliminate gearbox failure and downtime effects (gearboxes are liable to significant accumulated fatigue torque loading with relatively high maintenance costs), although it gets the shortcomings including low energy yield, poor power quality, significant audible noise and difficulties in braking the turbine [118,119]. ...
Wind, as a sustainable and affordable energy source, represents a strong alternative to traditional energy sources. However, wind power is only one of the options, together with other renewable energy sources. Consequently, the core concerns for wind turbine manufacturers and operators are to increase its reliability and decrease costs, therefore enhancing commercial competitiveness. Among typical failure modes of wind turbines, fatigue is a common and critical source. In view of the significance of fatigue reliability in wind turbine structural integrity, reliable probabilistic fatigue theories are necessary for design scheme optimization. By reducing the expenses on manufacturing, operation, and maintenance in reliability- and cost-optimal ways, the cost of energy can be significantly reduced. This study systematically reviews the state-of-the-art technology for fatigue reliability of wind turbines, and elaborates on the evolution of methodology in wind load uncertainty modelling. In addition, fatigue reliability assessment techniques on four typical components are summarized. Finally, discussions and conclusions are presented, intending to provide direct insights into future theoretical development and methodological innovation in this field.
... ARIMA, OLS, CUSUM), makes it possible to detect generator bearing failures with a high accuracy. Further information on the current state-of-the-art can be found in [22], [23]. ...
Full-text available
In this research an early warning methodological framework is developed that is able to detect premature failures due to excessive wear. The methodology follows the data-driven Normal Behavior Model (NBM) principle, in which one or more data-driven models are used to model the normal behavior of the wind turbine. Anomalous behaviour of the turbine is identified by analyzing the deviation between the observed and predicted normal behaviour. The framework consists of two pipelines, a statistics and machine learning based pipeline. The former is based on techniques like ARIMA, OLS and CUSUM. The latter makes use of techniques like Random Forest, Gradient Boosting, … Each pipeline has its strengths and weaknesses, but by combining them in an intelligent way, a more capable detector is developed. The methodology is validated on 10-minute SCADA data from a real operational wind farm. The validation case focuses on generator (front/rear) bearing failures. The goal is to predict these failures well in advance (ideally at least a month) using the developed framework, which should allow for timely adjustments to the maintenance plan. The results show that the methodology is able to accomplish this reliably.
... Electrostatic charging of the rotor blades or the presence of generators after the gearbox can cause electrical current flow at roller bearings. Furthermore, the trend towards more compact designs and mechanical integration of the main bearing, gearbox and generator [24] does not facilitate complete electrical insulation. In addition, converters are suspected to generate electrical current flows in roller bearings [25]. ...
White Etching Cracks (WEC) are currently discussed as a common cause for premature failure of roller bearings in various applications. The formation mechanism of WEC is still under debate in the literature. However, it is emphasized that varying additional loads like electrical current or hydrogen, have an amplifying effect on the formation of WEC. In this work, the formation of WEC under the influence of electrical current was investigated. The testing was conducted on a three-ring on roller test rig using rollers made from the steel AISI/SAE 52100. These rollers were tested utilising different electrical polarities, current intensities, Hertzian pressures and slide-roll-ratios. As a result of the testing, possible WEC formation values for the tested electrical intensities and Hertzian pressures were found. Furthermore, no additional slip has to be present for the WEC formation under the influence of electrical current. Detailed microstructure analysis using Scanning electron microscopy (SEM), electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM) have been conducted to investigate the effect of electrical current, polarisation and slide roll ratio on the microstructural alterations. The analyses showed that in the investigated regions different reaction layers are formed depending on the electrical polarity. Furthermore, the formation of the nanocrystalline structure can be attributed to high local plastic deformation.
... However, a new trend is directed for taller and larger wind turbines, such as PMSG-based ones, to extract more power and maximize energy captured via their associated full-scale converter systems [1]. This configuration aims to achieve balance between generator size and maintenance effort, where the need for a gearbox can be eliminated by using a high pole number-based PMSG [3,4]. A comparative study of DFIGs and PMSGs showed that during the machine early life, a PMSG has a failure rate 40% lower than that of a comparable DFIG [5]. ...
Full-text available
Low-voltage ride-through (LVRT) and grid support capability are becoming a necessity for grid-tied renewable energy sources to guarantee utility availability, quality and reliability. In this paper, a swap control scheme is proposed for grid-tied permanent magnet synchronous generator (PMSG) MW-level wind turbines. This scheme shifts system operation from maximum power point tracking (MPPT) mode to LVRT mode, during utility voltage sags. In this mode, the rectifier-boost machine-side converter overtakes DC-link voltage regulation independently of the grid-side converter. The latter attains grid synchronization by controlling active power injection into the grid to agree with grid current limits while supporting reactive power injection according to the sag depth. Thus grid code requirements are met and power converters safety is guaranteed. Moreover, the proposed approach uses the turbine-generator rotor inertia to store surplus energy during grid voltage dips; thus, there is no need for extra hardware storage devices. This proposed solution is applied on a converter topology featuring a minimal number of active switches, compared to the popular back-to-back converter topology. This adds to system compatibility, reducing its size, cost and switching losses. Simulation and experimental results are presented to validate the proposed approach during normal and LVRT operation.
... These bearings are typically called the "main" bearings and can either be located in a dedicated housing or integrated within the gearbox itself or a direct-drive generator. Main bearings do not have an application-specific design standard and are typically rated with respect to International Organization for Standardization (ISO) standards, technical specifications, or supplier specifications Nejad et al. 2021). Premature main bearing failures can be a significant operation and maintenance cost ), although failure rates can vary between populations (e.g., land-based or offshore, direct-drive or geared, site-to-site, drivetrain mounting style). ...
Technical Report
Full-text available
Premature main bearing failures can be a significant operation and maintenance cost, although failure rates can vary between populations (e.g., land-based or offshore, direct-drive or geared, site-to-site, drivetrain mounting style). Unlike most gearboxes, main bearings typically cannot be repaired uptower and often require crane removal, which results in appreciable downtime. Most failures are related to progressive wear stemming from micropitting, smearing, scuffing, skidding, or fretting rather than fatigue. Failure rates in some populations can be as high as 20%–30% in as little as 6–10 years, though they were designed for a rating life exceeding 20 years. Main bearing loads, including those induced by gravity, aerodynamic rotor thrust and side loads, and pitch and yaw moments, are the result of interactions between the rotor and the complex wind field in which it is operating. In this report, measurements of roller load-induced behavior on the instrumented spherical roller main bearing in the General Electric 1.5-megawatt (MW) SLE model turbine at the National Renewable Energy Laboratory (NREL) Flatirons Campus are described and used to examine typical roller loads, outer ring strain, and other factors that might impact main bearing health, such as, cage slip. These parameters are useful for comparison with bench-level tests of bearing contact conditions that have been shown to contribute to premature wear and fatigue.
... Wind turbine gearboxes used in horizontal axis wind turbine drivetrains continue to grow in size to up to three meters in diameter, power up to 15 MW, and torque-density of 200 newton-meters per kilogram (Vaes, Clement, and Lindstedt 2021;Daners and Nickel 2021;ZF 2021;Winergy 2021;Nejad et al. 2021). They are designed for a minimum 20-year design life as specified in the IEC 61400-4 and AGMA 6006 gearbox design standards. ...
Technical Report
Full-text available
The information exchanged and new R&D opportunities identified at the DRC workshops and meetings are valuable for prioritization of future R&D plans. The DRC workshops and meetings provide a venue for exchange of information in an open and transparent manner, with the common goals of improvement in wind turbine drivetrain reliability and reduction in wind plant O&M costs. The DRC also demonstrates how the industry may collaborate to close the gaps between design and manufacturing and field operational experience.
Full-text available
This work considers the characteristics and drivers of the loads experienced by wind turbine main-bearings. Simplified load response models of two different hub and main-bearing configurations are presented, representative of both inverting direct-drive and four-point mounted geared drivetrains. The influences of deterministic wind field characteristics, such as wind speed, shear, yaw offset and veer, on the bearing load patterns are then investigated for similarity scaled 5, 7.5 and 10 MW reference wind turbine models. Main-bearing load response in cases of deterministic gusts and extreme changes in wind direction are also considered for the 5 MW model. Perhaps surprisingly, veer is identified as an important driver of main-bearing load fluctuations. Upscaling results indicate that similar behaviour holds as turbines become larger, but with mean loads and load fluctuation levels increasing at least cubically with the turbine rotor radius. Strong links between turbine control and main-bearing load response are also observed.
Full-text available
In order to identify holistically better drivetrain concepts for onshore wind turbine application, their operational behavior needs to be considered in an early design phase. In this paper, a validated approach for estimating drivetrain-concept-specific unplanned operational effort and risk based on open-access data is presented. Uncertain influencing factors are described with distribution functions. This way, the poor data availability in the early design phase can be used to give an indication of the concept’s choice influence on the unplanned operational wind turbine behavior. In order to obtain representative comparisons, a Monte Carlo method is applied. Technical availability and drivetrain-influenced unplanned operational effort are defined as evaluation criteria. The latter is constituted by labor, material and equipment expenses. By calculating the range of fluctuation in the evaluation criteria mean values, this approach offers an indication of the inherent risk in the operational phase induced by the drivetrain concept choice. This approach demonstrates that open-access data or expert estimations are sufficient for comparing different drivetrain concepts over the operational phase in an early design stage when using the right methodology. The approach is applied on the five most common state-of-the-art drivetrain concepts. The comparison shows that among those concepts the drivetrain concept without a gearbox and with a permanent magnet synchronous generator performs the best in terms of absolute drivetrain-influenced unplanned operational effort over the drivetrain’s lifetime as well as in terms of the inherent risk for the assumptions made. It furthermore makes it possible to give insights into how the different drivetrain concepts might perform in future applications in terms of unplanned operational effort. Exemplarily the impacts of higher torque density in gearboxes, a change to moment bearings and adjusted coil design in electrically excited generators have been analyzed. This analysis shows that the superiority of synchronous-generator concepts manifested in historic data is not entirely certain in future applications. Concluding, this approach will help to identify holistically better wind turbine drivetrain concepts by being able to estimate the inherent risks and effort in the operational phase.
Full-text available
This paper presents lessons learned from own research studies and field experiments with drivetrains on floating wind turbines over the last ten years. Drivetrains on floating support structures are exposed to wave-induced motions in addition to wind loading and motions. This study investigates the drivetrain-floater interactions from two different viewpoints: how drivetrain impacts the sub-structure design; and how drivetrain responses and life are affected by the floater and support structure motion. The first one is linked to the drivetrain technology and layout, while the second question addresses the influence of the wave-induced motion. The results for both perspectives are presented and discussed. Notably, it is highlighted that the effect of wave induced motions may not be as significant as the wind loading on the drivetrain responses particularly in larger turbines. Given the limited experience with floating wind turbines, however, more research is needed. The main aim with this article is to synthesize and share own research findings on the subject in the period since 2009, the year that the first full-scale floating wind turbine, Hywind Demo, entered operation in Norway.
Full-text available
Life cycle assessment (LCA) is conducive to the change in the wind power industry management model and is beneficial to the green design of products. Nowadays, none of the LCA systems are for wind turbines and the concept of Internet of Things (IoT) in LCA is quite a new idea. In this paper, a four-layer LCA platform of wind turbines based on IoT architecture is designed and discussed. In the data transmission layer, intelligent sensing of wind turbines can be achieved and their status and location can be monitored. In the data transmission layer, the LCA platform can be effectively integrated with enterprise information systems through the object name service (ONS) and directory service (DS). In the platform layer, a model based on IMPACT 2002+ is developed, and four management modules are designed. In the application layer, different from other systems, energy payback time (EPBT) is selected as an important evaluation index for wind turbines. Compared with the existing LCA systems, the proposed system is specifically for wind turbines and can collect data in real-time, leading to improved accuracy and response time.
Full-text available
This paper aims to investigate the drivetrain load response caused by induction and wake steering control on two floating wind turbines (FWTs) in a wind farm. In this study, two DTU 10 MW turbines, supported on the nautilus floater, are modelled using FAST.Farm. The downstream turbine is placed at the distance of seven rotor diameters (D) from the upstream turbine in the positive wind direction. Partial wake shading is considered for wake steering control and full wake shading is considered for induction control. An ambient wind speed of 8m/s is used and a representative sea state is selected. The test cases are defined based on different blade pitch and yaw angles of the upstream turbine. Power generation of the offshore wind farm is studied under different test cases. A decouped analysis approach is used to investigate drivetrain response. Global responses are obtained from FAST.Farm. These loads are used as input of the 10 MW wind turbine drivetrain model for the gears and bearings load response analysis. Results show that both induction and wake steering control lead to a limited increase in power generation of the wind farm. Additionally, both control methods affect the drivetrain response statistics, while the features are different. This study facilitates a better understanding on drivetrain dynamic behaviour in a wind farm perspective, which serves as a reference for the wind farm optimizaton in the future.
Conference Paper
This paper aims to analyze the feasibility of establishing a dynamic drivetrain model from condition monitoring measurements. In this study SCADA data and further sensor data is analyzed from a 1.5MW wind turbine, provided by the National Renewable Energy Laboratory. A multibody model of the drivetrain is made and simulation based sensors are placed on bearings to look at the possibility to obtain geometrical and modal properties from simulation based vibration sensors. Results show that the axial proxy sensor did not provide any usable system information due to its application purpose. SCADA data did not meet the Nyquist frequency and cannot be used to determine geometrical or modal properties. Strain gauges on the shaft can provide the shaft rotational frequency, while torque and angular displacement sensors can provide the torsional eigenfrequency of the system. Simulation based vibration sensors are able to capture gear mesh frequencies, harmonics, sideband frequencies and shaft rotational frequencies.
Conference Paper
In this article a novel approach for the estimation of wind turbine gearbox loads with the purpose of online fatigue damage monitoring is presented. The proposed method employs a Digital Twin framework and aims at continuous estimation of the dynamic states based on CMS vibration data and generator torque measurements from SCADA data. With knowledge of the dynamic states local loads at gearbox bearings are easily determined and fatigue models are be applied to track the accumulation of fatigue damage. A case study using simulation measurements from a high-fidelity gearbox model is conducted to evaluate the proposed method. Estimated loads at the considered IMS and HSS bearings show moderate to high correlation (R = 0.50–0.96) to measurements, as lower frequency internal dynamics are not fully captured. The estimated fatigue damage differs by 5–15 % from measurements.
Conference Paper
The focused shift to reduce carbon emissions by substituting fossil fuels with renewable energy sources, including wind, is increasing. This means that more and more wind turbines are being installed, both onshore and offshore and as this number increases, more and more turbines are reaching their end of designed service life. Extending this designed service life, which is commonly referred to as lifetime extension (LTE), is particularly favoured by owner/operators, due to economic reasons. Whilst there are relatively well-established practices for lifetime extension of structural members or those preserving structural integrity, the electro-mechanical and drivetrain systems are often overlooked. Therefore, this paper reviews lifetime extension assessment practices executed within a variety of industries, such as oil and gas, marine vessels, electrical machines, mechanical rotating equipment and bearings, to determine if any of these practices can be implemented or adapted within the wind industry, particularly on wind turbine drivetrains.
This paper provides an analytical proof and the theoretical development of the idea of using the torsional vibration measurements for a system-level condition monitoring of the drivetrain system. The method relies on modal parameter estimation of the drivetrain system by using the torsional measurements and subsequent monitoring of the variations in the system eigenfrequencies and normal modes. Angular velocity error function extracted from encoder outputs at both input and output of drivetrain is used to estimate modal parameters including natural frequencies and damping coefficients. In the proposed condition monitoring approach, it is shown that any abnormal deviation from the reference values of the drivetrain system dynamic properties can be translated into the progression of a specific fault in the system. In order to extract the condition monitoring features, local sensitivity analysis is engaged to establish a relationship between different categories of drivetrain faults with the system dynamic properties and the amplitude of torsional response, which helps with both to identify the state of the progressive faults and to localize them. Local sensitive analysis shows that abnormal deviations in stiffness and moment of inertia due to the presence of faults result in considerable changes in natural frequencies and modal responses which can be measured and used as fault detecting features by using the proposed analytical approach. Sensitivity analysis is also employed along with the estimated modal frequency for estimation of modal damping from the amplitude of response at the natural frequencies and their subsequent use for estimation of undamped natural frequencies which are later used in the proposed condition monitoring approach. The proposed approach is computationally inexpensive and can be implemented without additional instrumentation. Two test cases, using 10 MW simulated and 1.75 MW operational drivetrains have been demonstrated.
Condition monitoring systems for manual transmissions based on vibration diagnostics are widely applied in industry. The systems deal with various condition indicators, most of which are focused on a specific type of gearbox fault. Frequently used condition indicators (CIs) are described in this paper. The ability of a selected condition indicator to describe the degree of gearing wear was tested using vibration signals acquired during durability testing of manual transmission with helical gears.
A smartphone is a low-cost pocket wireless multichannel multiphysical data acquisition system: the use of such a device for noise and vibration analysis is a challenging task. To what extent is it possible to carry out relevant analysis from it? The Survishno conference, held in Lyon in July 2019, proposed a contest to participants based on this subject. Two challenges were proposed, wherein each a mute video showing an object moving/excited at different frequencies was provided. Due to the frequencies set and the video sampling characteristics, special effects occurred and are visible on both videos. From the first video, participants were asked to estimate the Instantaneous Angular Speed (IAS) of a rotating fan. From the second video, they were asked to perform the modal analysis of a cantilever beam. This paper gathers the interesting ideas proposed by the contestants and proposes a global method to solve these two problems. One major point of the paper might be the advantageous use of the rolling shutter effect, a well-known artefact of smartphone videos, to perform advanced mechanical analyses: the consideration of the unavoidable slight phase shift between the acquisition of each pixel opens up the possibility to perform a dynamic analysis at frequencies that are much higher than the video frame rate.