Jan Helsen

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• Dr Ir
• Professor at Vrije Universiteit Brussel

We study day-ahead bidding strategies for wind farm operators under a one-price balancing scheme, prevalent in European electricity markets. In this setting, the profit-maximising strategy becomes an all-or-nothing strategy, aiming to take advantage of open positions in the balancing market. However, balancing prices are difficult, if not impossibl...

Power production of offshore wind farms depends on many parameters and is significantly affected by wake losses. Due to the variability in wind power and its rapidly increasing share in the total energy mix, accurate forecasting of the power production of a wind farm becomes increasingly important. This paper presents a novel data-driven methodolog...

This study presents a framework for estimating the full vibrational state of wind turbine blades from sparse deflection measurements. The identification is performed in a reduced-order space obtained from a Proper Orthogonal Decomposition (POD) of high-fidelity aeroelastic simulations based on Geometrically Exact Beam Theory (GEBT). In this space,...

Accurately assessing wind turbine performance in large offshore wind farms requires a nuanced understanding of how inflow parameters—turbulence intensity (TI), wind shear, and wind veer—affect power production across different turbine rows. In this study, we analyze 13 months of 10-minute operational data from more than 40 high-capacity turbines in...

The Multi-Order Probabilistic Approach (MOPA) is a well established method utilised for the estimation of instantaneous angular speed (IAS) based on vibration signals. However, it has some shortcomings and challenges, such as the selection of its input parameters and contamination from asynchronous harmonics. This work presents two suggestions for...

This work presents an advanced study on improving condition monitoring systems or CMS for wind turbine drivetrains through multi-modal data. We present an innovative AI-based CMS framework that integrates temperature monitoring, oil debris analysis, and vibration data into a comprehensive assessment of drivetrain health. By employing physics-based...

This study presents an applied system identification approach for developing, updating, and validating simulation models of wind turbines using field measurements. This is demonstrated by developing a model of a bottom-fixed offshore turbine in the Belgian North Sea. An initial model is obtained based on available design information and the scaling...

This study investigates the relationship between inflow properties of offshore wind turbines and fatigue damage accumulation in key machine components, specifically focusing on blade root and blade bearing fatigue. It showcases how LiDAR measurements can be used with a simulation model to provide fatigue damage estimations for a deployed turbine ba...

As wind turbines grow and wind farms become denser, more insight into real metocean conditions is essential for operational efficiency and load assessment. Light Detection And Ranging LiDAR) technology, which can substitute the use of meteorological masts, has garnered significant attention in the literature. However, it indirectly measures wind pa...

The application of in situ monitoring systems for part quality verification or process qualification of the laser based directed energy deposition process will require the ability to confidently detect (or estimate) the type, size, and location of defects in an additive manufactured component. These developments are requested by the industry to sup...

Wind energy is considered a sustainable renewable energy source; however, it faces the challenge of significant operating and maintenance costs. The research proposes a hybrid fault detection method to combine the physical domain knowledge with the machine learning models to provide an overview of the health of wind turbine drivetrain components. S...

The global rise in offshore wind farms underscores the need to cut costs and optimise energy production. As turbines increase in size and wind farms become more concentrated, mitigating downstream wake effects is crucial for operational efficiency. LiDAR technology, offering advantages like eliminating the need for meteorology masts, has been exten...

This study investigates the influence of wind turbine wakes on the incubation period of leading-edge erosion in offshore environments within an offshore wind cluster. The analysis is performed on a cluster of 250MW+ offshore wind farms mainly consisting of wind turbines with over 5MW rated power. The incubation time of the leading-edge erosion is d...

This study investigates the impact of operating and environmental conditions on the vibration induced on an offshore wind turbine drivetrain. Furthermore, it explores the role of the dynamic response of the global wind turbine structure to vibration on the drivetrain. A prolonged experimental campaign dedicated to condition monitoring the drivetrai...

This paper introduces a novel model for predicting wind turbine power output within a wind farm at a high temporal resolution of 30 seconds. The wind farm is represented as a graph, with Graph Neural Networks (GNNs) used to aggregate selected input features from neighboring turbines. A temporal component is added by feeding a timeseries of input fe...

Power production of offshore wind farms depends on many parameters and is significantly affected by wake losses. Due to the variability of wind power and its rapidly increasing share in the total energy mix, accurate forecasting of the power production of a wind farm becomes increasingly important. This paper presents a novel data-driven methodolog...

This work presents a robust methodology for calibrating analytical wake models, as demonstrated on the velocity deficit parameters of the Gauss–curl hybrid model using 4 years of time series supervisory control and data acquisition (SCADA) data from an offshore wind farm, with a tree-structured Parzen estimator employed as a sampler. Initially, a s...

Modern research endeavors in wind energy have been increasingly focused on achieving accurate representations of wind turbine loading across diverse atmospheric conditions. Recent advancements in numerical weather prediction techniques make it possible to downscale weather conditions for operational use, underscoring the importance of including air...

This study benchmarks the performance of multiple analytical wake models using a multi-level hyperparameter optimization framework for calibrating models with SCADA data in the Belgian-Dutch offshore zone. The calibration targets wind coming the northwest (300 to 330 degrees) with wind speeds ranging from 7 to 9 m/s, a wind direction where wakes ar...

Rain-driven wind turbine blade erosion, particularly in offshore locations, has been observed as early as within 5 to 7 years of turbine operation, which is below the lifetime expectancy design age of 20 to 25 year. Due to the harsh atmospheric conditions offshore, the preservation of wind turbine blade integrity has become a fundamental necessity....

Wind farms usually comprise several turbines of the same type in proximity to one another. Therefore, similarities exist between the power production of specific turbines within the wind farm over time. Considering this, it is possible to find a way to express the similarity between turbines and exploit their properties to find a formulation of the...

This study investigates the influence of inflow characteristics on wind turbine power production using Detrended Fluctuation Analysis (DFA). The research focuses on extracting indicators for the energy of flow fluctuations at small timescales and their scaling properties. This methodology is demonstrated using one year of 1-second SCADA data from a...

Accurate loss estimation methods with a high level of temporal granularity are necessary to enable the implementation of efficient and adaptable control strategies for wind farms. Predictive models for the power of wind turbines within a wind farm are investigated using high-resolution SCADA data and deep learning methodologies. Traditional physica...

A multi-level hyperparameter optimization framework is performed to calibrate analytical wake models in the context of multiple wind farms within the Belgian-Dutch offshore cluster. The calibration, applied on the TurbOPark model with Gaussian wake profile, is performed on different scales. Initially, calibration focused solely on internal wake eff...

This study details the development of a fully automated pipeline for the condition monitoring of wind turbine drive trains. Vibration data is collected using hardware designed and manufactured in-house and used directly to monitor the condition of the drive trains. The complex nature of wind turbine vibration signals, due to the large number of com...

Metal Additive Manufacturing processes such as Directed Energy Deposition (DED) require process monitoring to ensure the highest part quality. Detecting and avoiding material defects to meet high material requirements remains a challenge due to the complexity of the process. To address this challenge, this study presents a novel approach that combi...

Power production of offshore wind farms depends on many parameters and is significantly affected by wake losses. Due to the intermittency of wind power and its rapidly increasing share in the total energy mix, accurate forecasting of wind farm power production becomes increasingly important. This paper presents a data-driven methodology for forecas...

Disclosure: L. Van der Veken: None. X. Chestermann: None. K. Casteels: None. J. Helsen: None. A.M. Rochtus: None. Childhood obesity is a growing problem worldwide and can lead to type 2 diabetes, hypertension, dyslipidemia, and carotid-artery atherosclerosis. This is known as “metabolically unhealthy obesity” (MUO). However, a subgroup of children...

The maintenance of wind turbines is essential to reduce wind energy levelized costs. Earlier detection of potential faults in the rotating subcomponents, such as the drivetrain, helps to plan maintenance actions. Several vibration processing methods, e.g., short-time Fourier analysis, are available in the literature to detect faults, however, they...

Research nowadays takes advantage of the cyclostationary properties in the vibration waveforms of rotating machines for fault detection. For example, cyclic spectral coherence maps (CSCM) break down vibration signals into cyclic and carrier frequencies. However, the large size of the CSCMs and the enormous amount of data makes it challenging to ide...

Calibrating analytical wake models for wind farm yield assessment and wind farm flow control presents significant challenges. This study provides a robust methodology for the calibration of the velocity deficit parameters of an analytical wake model. Initially, a sensitivity analysis of wake parameters of the Gauss-Curl Hybrid model and their linea...

Vibration analysis is a prevalent technique in the predictive maintenance of wind turbines. It is an effective method for early fault detection and enables the creation of cost-effective maintenance strategies. Commonly used vibration analysis methods in the literature rely on signal processing techniques such as time and frequency domain approache...

Wind is a renewable energy source that has become more important in recent years. Wind turbines are equipped with a SCADA system, which allows for remote supervision of the wind farm. SCADA systems are customarily used to provide data averaged every 10 minutes. Nevertheless, recent literature suggests that more insights could be extracted with a hi...

Condition monitoring and failure prediction for wind turbines currently comprise a hot research topic. This follows from the fact that investments in the wind energy sector have increased dramatically due to the transition to renewable energy production. This paper reviews and implements several techniques from state-of-the-art research on conditio...

The experimental evaluation of melt surface flow in blown laser cladding and additive manufacturing usually employs tracer particles added to the powder feed. This paper presents details of a high-speed imaging (HSI) and image processing technique, which can directly monitor the flow of standard (nontracer) particles on the surface of the melt. Thi...

Curtailment is a known phenomenon for wind turbine operators of both onshore and offshore wind turbine generators (WTG). Curtailment refers to the situation in which the power output of all WTG’s within a windfarm is forced below the expected power output at the occurring environmental conditions. A direct consequence of curtailment is the loss of...

The profitability of wind turbine energy production is for an important part determined by the operation and maintenance costs of wind turbines. An important driver of these costs is currently the premature failure of components due to excessive wear. If it would be possible to accurately predict these failures, preventive maintenance can be made m...

In this paper, the load effect of torque ripple reduction of a wind turbine generator is analyzed on the high-speed shaft gear stage and high-speed shaft bearings, which are the nearest components to the generator. Two generator designs with different torque ripples for the NREL 5‑MW reference wind turbine are considered. A decoupled analysis metho...

Drivetrain failures result in the largest downtime per failure among the different turbine components. To minimize O&M costs, it is therefore essential to be able to anticipate failure events sufficiently in advance such that scheduled maintenance can take place. Moreover, a root cause for the failure should be identified, allowing to incorporate t...

Condition monitoring and failure prediction for wind turbines is currently a hot research topic. This follows from the fact that investments in the wind energy sector have increased dramatically due to the transition to renewable energy production. This paper reviews and implements several techniques from state-of-the-art research on condition moni...

The development of a reliable and automated condition monitoring methodology for the detection of mechanical failures in rotating machinery has garnered much interest in recent years. Thanks to the rise in popularity of machine learning techniques, the number of purely data-driven approaches that try to tackle the issue of vibration-based condition...

One of today's ongoing challenges in directed energy deposition (DED) is controlling the geometry and material properties of parts. The objective of this paper is to investigate the relationship between several printing parameters of DED (laser power, laser speed, powder feed rate) and the melt pool temperature. Because DED is a complex and nonline...

This study attempts to improve the performance of Generalized Likelihood Ratio Test-based indicators via blind filtering the of vibration signals. The key point is the optimization of the filter coefficients to maximize the indicator of interest. The filter coefficients are optimized through Rayleigh quotient iteration. The proposed method's perfor...

Renewable energy is an essential driver towards tackling the climate crisis. Given the increasing need to ensure sustainability and reduce greenhouse gas emissions, wind energy has become one of the most relevant areas to consider in today’s society. However, it appears challenging to rely on this type of energy because of the uncertain nature of t...

Vibration signals measured on rotating machinery typically exhibit cyclostationarity due to the inherent nature of real-world rotating vibration sources. Hence, the development of signal processing tools devoted to investigating or exploiting this cyclostationarity for condition monitoring purposes of gears and bearings has seen a significant incre...

Studies in condition monitoring literature often aim to detect rolling element bearing faults because they have one of the biggest shares among defects in turbo machinery. Accordingly, several prognosis and diagnosis methods have been devised to identify fault signatures from vibration signals. A recently proposed method to capture the rolling elem...

The Weather Research and Forecasting (WRF) model offers a multitude of physics parameterizations to study and analyze the different atmospheric processes and dynamics that are observed in the Earth's atmosphere. However, the suitability of a WRF model setup is known to be highly sensitive to the type of weather phenomena and the type and combinatio...

Deep learning methods have become popular among researchers in the field of fault detection. However, their performance depends on the availability of big datasets. To overcome this problem researchers started applying transfer learning to achieve good performance from small available datasets, by leveraging multiple prediction models over similar...

The short-time Fourier transform (STFT) is a staple analysis tool for vibration signal processing due to it being a robust, non-parametric, and computationally efficient technique to analyze non-stationary signals. However, despite these beneficial properties, the STFT suffers from high variance, high sidelobes, and a low resolution. This paper inv...

Extreme weather events such as storms, cold fronts, and mesoscale convective systems, are capable of producing extreme and sudden precipitation, strong wind gusts and fast changes in wind direction, which are potentially harmful for the operation, power production and maintenance of wind farms. This study aims to provide insight into the modelling...

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...

Phase demodulation is arguably the most used technique for the estimation of the instantaneous angular speed from vibration signals measured on rotating machinery. Although phase demodulation offers a straightforward approach to determine accurately the rotation speed of a particular shaft in a rotating machine, it does have strict limitations that...

This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the system that converts 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 dig...

Operational Modal Analysis allows to assess the modal model of rotating machinery. However, it is needed to pre-process the measured vibration data such that the influence of the harmonic content is suppressed. In this paper, such pre-processing techniques are discussed. The main focus is on evaluating the effects of amplitude modulation, originati...

The Weather, Research and Forecasting (WRF) model includes a multitude of physics parameterizations to account for atmospheric dynamics and interactions such as turbulent fluxes within the planetary boundary layer (PBL), long and short wave radiation, hydrometeor representation in microphysics, cloud ensemble representation in cumulus, amongst othe...

Premature failures caused by excessive wear are responsible for a large fraction of the maintenance costs of wind turbines. Therefore, it is crucial to be able to identify the propagation of these failures as early as possible. To this end, a novel condition monitoring method is proposed that uses statistical data analysis techniques and machine le...

Detection of bearing faults is a challenging task since the impulsive pattern of bearing faults often fades into the noise. Moreover, tracking the health conditions of rotating machinery generally requires the characteristic frequencies of the components of interest, which can be a cumbersome constraint for large industrial applications because of...

One of today’s ongoing challenges in directed energy deposition (DED) is controlling the geometry and material properties of parts. This manufacturing process is complex and nonlinear due to multiple physical phenomena at play and is therefore hard to model analytically. Machine learning (ML) on the contrary is particularly well suited to predict t...

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. Offsh...

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 place...

Drivetrains play an essential role in guaranteeing the reliability of wind turbines. A challenge in their design is the wide range of loading conditions they are exposed to. Several design load cases are required to be simulated in order to ensure that the ultimate loads are not exceeded, and to validate that the fatigue lifetime matches the design...

Roller element bearings present in the intermediate and high-speed stages of wind turbine gearboxes operate in dynamic working conditions and in some cases may fail within 30% or less of their designed lifetime. Upon investigation, it has been identified that these premature failures happen due to a peculiar failure mode associated with formation o...

This paper discusses trends in condition monitoring of modern offshore wind turbines. First an overview is given of design changes that have been made over the years to large offshore wind turbines and how this resulted in novel opportunities from a monitoring perspective. Similarly, the evolution in data source availability is discussed. From thes...

This paper investigates the efficacy and reliability of three different state-of-the-art rotation speed estimation techniques on a very large set of experimental vibration data originating from thirty offshore wind turbine gearboxes. The three methods include the multi-order probabilistic approach, the phase demodulation method based on the frequen...

The Integrated Electronics Piezo-Electric (IEPE) standard is a common interfacing strategy for industrial accelerometers that is used for vibration and acoustic measurements. The IEPE interface is an untapped resource with opportunities for expanded sensing, including measurements with frequency content down to DC like temperature or pressure. This...

Wind farms are a crucial driver toward the generation of ecological and renewable energy. Due to their rapid increase in capacity, contemporary wind farms need to adhere to strict constraints on power output to ensure stability of the electricity grid. Specifically, a wind farm controller is required to match the farm's power production with a powe...

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 su...

Due to changes in generator topology, wind turbines are operating in much wider speed ranges and thus at more varying operating conditions. This has a positive influence on the energy production, but results in much wider gear mesh excitation ranges that can lead to tonalities. As such, a high-quality characterization of the modal model of the gear...

Often, the condition of a machine tool is detected indirectly in the reduced quality of manufactured parts upon visual inspection. Reliable and efficient machine tool condition monitoring is indispensable for manufacturing. Further, issues affecting machine tools are closely related to pathologies associated with many other industrial electromechan...

Wind energy is one of the largest sources of renewable energy in the world. To further reduce the operations and maintenance (O&M) costs of wind farms, it is essential to be able to accurately pinpoint the root causes of different failure modes of interest. An example of such a failure mode that is not yet fully understood is white etching cracks (...

A multi-physical time- and frequency-domain fault detection and isolation (FDI) method for power-electronic converters (PECs) of wind turbines is presented in this paper, with focus on open-circuit faults and the doubly-fed induction generator (DFIG) drivetrain topology. In order to reduce the risk of false alarms, the proposed approach combines fa...

This paper proposes a signal processing approach for wind turbine gearbox vibration signals based on employing multiple analysis pipelines. These so-called pipelines consist of combinations of various advanced signal processing methods that have been proven to be effective in literature when applied to wind turbine vibration signals. The performanc...

Multi-agent coordination is prevalent in many real-world applications. However, such coordination is challenging due to its combinatorial nature. An important observation in this regard is that agents in the real world often only directly affect a limited set of neighbouring agents. Leveraging such loose couplings among agents is key to making coor...

This paper investigates a novel perspective on blind filtering of vibration signals with the purpose of fault detection in rotating machinery. Instead of maximizing a property of the time-domain signal such as kurtosis to find an optimal filter, the sparsity of its envelope spectrum is maximized. The underlying assumption for this approach is that...