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Condition monitoring and fault diagnosis of wind turbines based on structural break detection in SCADA data

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  • Dao, Phong B.

Abstract

This paper presents a new approach – based on structural break detection in SCADA data – for condition monitoring and fault diagnosis of wind turbines. Chow test – a commonly used testing method for structural breaks in economic and financial data – has been adapted for this purpose. A five-step Chow's test-based computation procedure is proposed. The method is applied for condition monitoring of a wind turbine drivetrain with a nominal power of 2 MW using temperature-related SCADA data. A multiple linear regression model is formed using gearbox and generator temperature data as the independent variables and generator speed data as the dependent variable. The Chow test is used to assess the stability of regression coefficients in the model. Any coefficient instability means a structural change in the regression model, which can be interpreted as the occurrence of a fault in the wind turbine. The scheme used to detect structural changes is based on control charts, where sequences of the calculated probability (p-values) are plotted together with the critical line, defined by the significance level. The method is validated using two known fault events. The results demonstrate that the proposed method can effectively monitor the wind turbine and reliably detect abnormal problems.

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  • Dao, Phong B., 2022. "Condition monitoring and fault diagnosis of wind turbines based on structural break detection in SCADA data," Renewable Energy, Elsevier, vol. 185(C), pages 641-654.
    • Handle: RePEc:eee:renene:v:185:y:2022:i:c:p:641-654
    DOI: 10.1016/j.renene.2021.12.051
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    Cited by:

    1. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2023. "A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data," Renewable Energy, Elsevier, vol. 208(C), pages 420-430.
    2. Zhan, Jun & Wu, Chengkun & Yang, Canqun & Miao, Qiucheng & Wang, Shilin & Ma, Xiandong, 2022. "Condition monitoring of wind turbines based on spatial-temporal feature aggregation networks," Renewable Energy, Elsevier, vol. 200(C), pages 751-766.
    3. Dao, Phong B., 2022. "On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines," Applied Energy, Elsevier, vol. 318(C).
    4. Emilio Ramírez-Juidías & José-Lázaro Amaro-Mellado & Jorge Luis Leiva-Piedra, 2022. "Influence of the Urban Green Spaces of Seville (Spain) on Housing Prices through the Hedonic Assessment Methodology and Geospatial Analysis," Sustainability, MDPI, vol. 14(24), pages 1-15, December.
    5. Dao, Phong B. & Barszcz, Tomasz & Staszewski, Wieslaw J., 2024. "Anomaly detection of wind turbines based on stationarity analysis of SCADA data," Renewable Energy, Elsevier, vol. 232(C).
    6. Dhibi, Khaled & Mansouri, Majdi & Bouzrara, Kais & Nounou, Hazem & Nounou, Mohamed, 2022. "Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems," Renewable Energy, Elsevier, vol. 194(C), pages 778-787.
    7. Tao, Cheng & Tao, Tao & He, Shukai & Bai, Xinjian & Liu, Yongqian, 2024. "Wind turbine blade icing diagnosis using B-SMOTE-Bi-GRU and RFE combined with icing mechanism," Renewable Energy, Elsevier, vol. 221(C).
    8. Luo, Yi & Zhao, Xiujie & Liu, Bin & He, Shuguang, 2024. "Condition-based maintenance policy for systems under dynamic environment," Reliability Engineering and System Safety, Elsevier, vol. 246(C).

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