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Detection and classification of faults in pitch-regulated wind turbine generators using normal behaviour models based on performance curves

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  • Bi, Ran
  • Zhou, Chengke
  • Hepburn, Donald M.

Abstract

The fast growing wind industry requires a more sophisticated fault detection approach in pitch-regulated wind turbine generators (WTG), particularly in the pitch system that has led to the highest failure frequency and downtime. Improved analysis of data from Supervisory Control and Data Acquisition (SCADA) systems can be used to generate alarms and signals that could provide earlier indication of WTG faults and allow operators to more effectively plan Operation and Maintenance (O&M) strategies prior to WTG failures. Several data-mining approaches, e.g. Artificial Neural Network (ANN), and Normal Behaviour Models (NBM) have been used for that purpose. However, practical applications are limited because of the SCADA data complexity and the lack of accuracy due to the use of SCADA data averaged over a period of 10 min for ANN training. This paper aims to propose a new pitch fault detection procedure using performance curve (PC) based NBMs. An advantage of the proposed approach is that the system consisting of NBMs and criteria, can be developed using technical specifications of studied WTGs. A second advantage is that training data is unnecessary prior to application of the system. In order to construct the proposed system, details of WTG operational states and PCs are studied. Power-generator speed (P-N) and pitch angle-generator speed (PA-N) curves are selected to set up NBMs due to the better fit between the measured data and theoretical PCs. Six case studies have been carried out to show the prognosis of WTG fault and to demonstrate the feasibility of the proposed method. The results illustrate that polluted slip rings and the pitch controller malfunctions could be detected by the proposed method 20 h and 13 h earlier than by the AI approaches investigated and the existing alarm system. In addition, the proposed approach is able to explain and visualize abnormal behaviour of WTGs during the fault conditions.

Suggested Citation

  • Bi, Ran & Zhou, Chengke & Hepburn, Donald M., 2017. "Detection and classification of faults in pitch-regulated wind turbine generators using normal behaviour models based on performance curves," Renewable Energy, Elsevier, vol. 105(C), pages 674-688.
    • Handle: RePEc:eee:renene:v:105:y:2017:i:c:p:674-688
    DOI: 10.1016/j.renene.2016.12.075
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    References listed on IDEAS

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    1. Cross, Philip & Ma, Xiandong, 2014. "Nonlinear system identification for model-based condition monitoring of wind turbines," Renewable Energy, Elsevier, vol. 71(C), pages 166-175.
    2. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    3. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
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    1. Sales-Setién, Ester & Peñarrocha-Alós, Ignacio, 2020. "Robust estimation and diagnosis of wind turbine pitch misalignments at a wind farm level," Renewable Energy, Elsevier, vol. 146(C), pages 1746-1765.
    2. Usama Aziz & Sylvie Charbonnier & Christophe Berenguer & Alexis Lebranchu & Frederic Prevost, 2022. "A Multi-Turbine Approach for Improving Performance of Wind Turbine Power-Based Fault Detection Methods," Energies, MDPI, vol. 15(8), pages 1-21, April.
    3. Xiaoyi Qian & Yuxian Zhang & Mohammed Gendeel, 2019. "State Rules Mining and Probabilistic Fault Analysis for 5 MW Offshore Wind Turbines," Energies, MDPI, vol. 12(11), pages 1-18, May.
    4. Cheng Xiao & Zuojun Liu & Tieling Zhang & Lei Zhang, 2019. "On Fault Prediction for Wind Turbine Pitch System Using Radar Chart and Support Vector Machine Approach," Energies, MDPI, vol. 12(14), pages 1-18, July.
    5. Zuojun Liu & Cheng Xiao & Tieling Zhang & Xu Zhang, 2020. "Research on Fault Detection for Three Types of Wind Turbine Subsystems Using Machine Learning," Energies, MDPI, vol. 13(2), pages 1-21, January.
    6. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
    7. Morrison, Rory & Liu, Xiaolei & Lin, Zi, 2022. "Anomaly detection in wind turbine SCADA data for power curve cleaning," Renewable Energy, Elsevier, vol. 184(C), pages 473-486.
    8. Aziz, Usama & Charbonnier, Sylvie & Bérenguer, Christophe & Lebranchu, Alexis & Prevost, Frederic, 2021. "Critical comparison of power-based wind turbine fault-detection methods using a realistic framework for SCADA data simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    9. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    10. Hong Wang & Hongbin Wang & Guoqian Jiang & Jimeng Li & Yueling Wang, 2019. "Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling," Energies, MDPI, vol. 12(6), pages 1-22, March.
    11. Suo Li & Ling-ling Huang & Yang Liu & Meng-yao Zhang, 2021. "Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines," Energies, MDPI, vol. 14(4), pages 1-16, February.

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