PosterPDF Available

Abstract

The wind industry has been growing significantly over the past decades, resulting in a remarkable increase in installed wind power capacity. Turbine technologies are rapidly evolving in terms of complexity and size, and there is an urgent need for cost effective operation and maintenance (O&M) strategies. Especially unplanned downtime represents one of the main cost drivers of a modern wind farm. Here, reliability and failure prediction models can enable operators to apply preventive O&M strategies rather than corrective actions. In order to develop these models, the failure rates and downtimes of wind turbine (WT) components have to be understood profoundly. This paper is focused on tackling three of the main issues related to WT failure analyses. These are, the non-uniform data treatment, the scarcity of available failure analyses, and the lack of investigation on alternative data sources. For this, a modernised form of an existing WT taxonomy is introduced. Additionally, an extensive analysis of historical failure and downtime data of more than 4300 turbines is presented. Finally, the possibilities to encounter the lack of available failure data by complementing historical databases with Supervisory Control and Data Acquisition (SCADA) alarms are evaluated.
Wind Turbine Failures - Tackling current
Problems in Failure Data Analysis
Maik Reder, Elena Gonzalez and Julio J. Melero
CIRCE - Universidad Zaragoza
Abstract
Results Failure Data Analysis
Unplanned wind turbine (WT) downtime represents one of the main cost drivers of a
modern wind farm. In order to avoid unplanned downtimes, reliability and failure
prediction models can enable operators to apply preventive O&M strategies rather
than corrective actions. In order to develop these models, the failure rates and
downtime of WT components have to be understood profoundly. This work focuses
on tackling three of the main issues related to WT failure analyses, see e.g. [1]:
1. Non-Uniform Data Treatment
2. Lack of Failure Data Analyses
3. Discovering alternative Data Sources
Methodology
Taxonomy - Uniformity in Data Treatment
Several different approaches have been established over the past years to classify
components regarding their physical location and functionality (e.g. [2], [3], [4], [5]).
However, there is a significant need for verification and modernisation. The table
below presents the modernised taxonomy developed for this study.
This project has received funding from the European Union's Horizon 2020 research
and innovation programme under the Marie Skłodowska-Curie grant agreement No
642108.
Databases
Table 1: Composition of the Database for the Failure Analysis
Avg. number of Wind Turbines considered per year
4300
WTs under 1 MW
2130
WTs equal or over 1 MW
2270
Direct drive turbines
215
Mean
yearly installed capacity (MW)
5818
Registered
Failure Events
7000
Observation
period
3
years
Table 3: Results for the three different turbine technologies
WT Technology
Failures/
Turb./Year
Downtime/
Turb
./Year
Downtime/Failure
Geared WTs <
1MW
0.46
78.46
h
151,46
h
Geared WTs ≥
1MW
0.52
44.51
h
112.67
h
Direct Drive 0.3
- 2MW
0.19
20.50
h
34.98
h
Conclusion and Outlook
Table 2: Composition of the SCADA Data Base
SCADA
System
Technology
Rated Capacity
(kW
)
Nb of
WTs
Failures per
WT
Alarms
per WT
1
Geared
1500
55
0.709
4170.07
2
Dir. Drive
2000
57
0.632
1120.35
3
Geared
850
77
2.208
2778.78
4
Geared
2000
168
1.780
4704.57
5
Geared
1800 & 2000
83
1.313
572.14
Failure rates and downtimes for three different WT setups have been analysed
based on a portfolio of 4300 WTs of a wide range of ages and rated capacities.
A smaller part of the data was used to correlate the amount of the component
related alarms with actual failures, showing how much information the different
SCADA systems contain for each component respectively to its failures.
Especially alarms related to harsh environmental conditions showed to be causing
pitch system, frequency converter and blade failures.
In future studies, the authors will focus on extending the WT failure analysis and
strategies using SCADA alarms in reliability modelling will be developed. Also,
environmental conditions before failures will be analysed in more detail.
Acknowledgements
References
[1] Kuik G A M V, Peinke J, Nijssen R, Lekou D, Mann J, Ferreira C, Wingerden J W V, Schlipf D, Gebraad P, Polinder H, Abrahamsen A,
Bussel G J W V, Tavner P, Bottasso C L, Muskulus M, Matha D, Lindeboom H J, Degraer S, Kramer O, Lehnhoff S, Sonnenschein M,
Morthorst P E and Skytte K 2016 Wind Energy Science 1 1-39
[2] Hill R R, Peters V, Stinebaugh J and Veers P S 2009 Sandia Report: Wind Turbine Reliability Database Update Tech. Rep. March Sandia
National Laboratories
[3] Stenberg A 2011 International Statistical Analysis on Wind Turbine Failures (Kassel, Germany) pp 117-122
[4] VGB-PowerTech 2014 VGB-Standard RDS-PP Application Specif. Part 32: Wind energy Tech. rep. VGB-PowerTech Essen, Germany
[5] Wilkinson M, Hendriks B, Spinato F and Van Delft T 2011 European Wind Energy Association Conference pp 1-8
[6] Gonzalez E, Reder M and Melero J J 2016 Journal of Physics: Conference Series Manuscript accepted for publication
Pre-Processing
Failure Logbooks Cleaning & Classification
Wind Turbine Taxonomy
Failure Analysis
SCADA Alarms
Association
Cleaning & Classification
Component Related
Failures and SCADA Alarms
Analysis
The following figures show the possible and the actually recorded SCADA alarms and
the contribution to the component related alarms and failures.
Contact Information:
Maik Reder
CIRCE-Universidad de Zaragoza, C/ Mariano Esquillor 15, 50018, Zaragoza, Spain
E-mail: mreder@fcirce.es
Results SCADA Data Analysis
Subsystem
Assembly
Subsystem
Assembly
Subsystem
Assembly
Power Module
Control &
Communications
Auxiliary
System
Frequency Converter
Sensors
Cooling system
Generator
Controller
Electrical Protection and Safety
Switch Gear
Communication System
Human Safety
Soft Starter
Emerg
. Contr. & Comm.
Series
Hydraulic Group
MV/LV Transformer
Data Aquisition System
WTG Meteorological Station
Power Feeder Cables
Nacelle
Lightning Protection
Power Cabinet
Yaw System
Firefighting System
Power Module Other
Nacelle Cover
Cabinets
Power Protection Unit
Nacelle Bed plate
Service Crane
Rotor & Blades
Drive train
Lift
Pitch System
Gearbox
Grounding
Other Blade Brake
Main Bearing
Beacon/Lights
Rotor
Bearings
Power Supply Auxiliary Systems
Blades
Mechanical Brake
Electrical Auxiliary Cabling
Hub
High Speed Shaft
Structure
Blade Bearing
Silent Blocks
Tower
Low Speed (Main) Shaft
Foundations
As an example the results for the share of component failures and caused downtime
to the respective total for Geared WTs 1MW are presented here.
Download the full paper here: Presented at:
... National and international initiatives have been mainly directed at creating repositories for onshore wind turbines [2]. Only recently have some initiatives focused on the collection of RAM data from modern [3,4] and/or offshore systems [5,6]. Although large and heterogeneous, the populations of some of the most well-known campaigns (e.g. ...
... Another two, more recent, sophisticated and comprehensive classifications are the ReliaWind project taxonomy (see e.g. Ref. [4,8]) and the RDS-PP® (Reference Designation System for Power Plants) taxonomy, published by the VGB PowerTech e.V. in Ref. [87]. The first was created for an extensive failure data analysis and is internationally recognised, while the second one, also widely accepted, is currently employed for the offshore data collection schemes of SPARTA and WinD-Pool (see Section 2.1.2). ...
... [65]), or, conversely, to account for alarm logs and remote resets (e.g. Ref. [4,39]). When counting remote resets, the failure rates recorded are higher. ...
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  • A Stenberg
Stenberg A 2011 International Statistical Analysis on Wind Turbine Failures (Kassel, Germany) pp 117-122
  • R R Hill
  • V Peters
  • J Stinebaugh
  • P Veers
Hill R R, Peters V, Stinebaugh J and Veers P S 2009 Sandia Report: Wind Turbine Reliability Database Update Tech. Rep. March Sandia National Laboratories
  • G A M V Kuik
  • J Peinke
  • R Nijssen
  • D Lekou
  • J Mann
  • C Ferreira
  • J W V Wingerden
  • D Schlipf
  • P Gebraad
  • H Polinder
  • A Abrahamsen
  • G J W V Bussel
  • P Tavner
  • C L Bottasso
  • M Muskulus
  • D Matha
  • H J Lindeboom
  • S Degraer
  • O Kramer
  • S Lehnhoff
  • M Sonnenschein
  • P E Morthorst
  • K Skytte
Kuik G A M V, Peinke J, Nijssen R, Lekou D, Mann J, Ferreira C, Wingerden J W V, Schlipf D, Gebraad P, Polinder H, Abrahamsen A, Bussel G J W V, Tavner P, Bottasso C L, Muskulus M, Matha D, Lindeboom H J, Degraer S, Kramer O, Lehnhoff S, Sonnenschein M, Morthorst P E and Skytte K 2016 Wind Energy Science 1 1-39
  • Vgb-Powertech
VGB-PowerTech 2014 VGB-Standard RDS-PP Application Specif. Part 32: Wind energy Tech. rep. VGB-PowerTech Essen, Germany
  • M Wilkinson
  • B Hendriks
  • F Spinato
  • T Van Delft
Wilkinson M, Hendriks B, Spinato F and Van Delft T 2011 European Wind Energy Association Conference pp 1-8