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Research on Fault Detection for Three Types of Wind Turbine Subsystems Using Machine Learning

Author

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  • Zuojun Liu

    (School of Control Science and Engineering, Hebei University of Technology, Tianjin 300131, China)

  • Cheng Xiao

    (School of Control Science and Engineering, Hebei University of Technology, Tianjin 300131, China
    School of Electronic and Control Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China)

  • Tieling Zhang

    (Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW 2522, Australia)

  • Xu Zhang

    (Department of Technical Development, AT&M Environmental Engineering Technology Co., Ltd., Beijing 100801, China)

Abstract

In wind power generation, one aim of wind turbine control is to maintain it in a safe operational status while achieving cost-effective operation. The purpose of this paper is to investigate new techniques for wind turbine fault detection based on supervisory control and data acquisition (SCADA) system data in order to avoid unscheduled shutdowns. The proposed method starts with analyzing and determining the fault indicators corresponding to a failure mode. Three main system failures including generator failure, converter failure and pitch system failure are studied. First, the indicators data corresponding to each of the three key failures are extracted from the SCADA system, and the radar charts are generated. Secondly, the convolutional neural network with ResNet50 as the backbone network is selected, and the fault model is trained using the radar charts to detect the fault and calculate the detection evaluation indices. Thirdly, the support vector machine classifier is trained using the support vector machine method to achieve fault detection. In order to show the effectiveness of the proposed radar chart-based methods, support vector regression analysis is also employed to build the fault detection model. By analyzing and comparing the fault detection accuracy among these three methods, it is found that the fault detection accuracy by the models developed using the convolutional neural network is obviously higher than the other two methods applied given the same data condition. Therefore, the newly proposed method for wind turbine fault detection is proved to be more effective.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:460-:d:310000
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    References listed on IDEAS

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    1. Yingying Zhao & Dongsheng Li & Ao Dong & Dahai Kang & Qin Lv & Li Shang, 2017. "Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data," Energies, MDPI, vol. 10(8), pages 1-17, August.
    2. 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.
    3. 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.
    4. Pinar Pérez, Jesús María & García Márquez, Fausto Pedro & Tobias, Andrew & Papaelias, Mayorkinos, 2013. "Wind turbine reliability analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 463-472.
    5. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
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    Cited by:

    1. Jersson X. Leon-Medina & Francesc Pozo, 2023. "Moving towards Preventive Maintenance in Wind Turbine Structural Control and Health Monitoring," Energies, MDPI, vol. 16(6), pages 1-4, March.
    2. 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.
    3. Ali Fayazi & Hossein Ghayoumi Zadeh & Hossein Ahmadian & Mahdi Ghane & Omid Rahmani Seryasat, 2024. "Pitch Actuator Fault-Tolerant Control of Wind Turbines via an L 1 Adaptive Sliding Mode Control ( SMC ) Scheme," Energies, MDPI, vol. 17(16), pages 1-20, August.
    4. Gisela Pujol-Vazquez & Leonardo Acho & José Gibergans-Báguena, 2020. "Fault Detection Algorithm for Wind Turbines’ Pitch Actuator Systems," Energies, MDPI, vol. 13(11), pages 1-14, June.

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