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On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines

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  • Dao, Phong B.

Abstract

In this study, the Wilcoxon rank sum test, a nonparametric statistical test method in the field of statistics, has been exploited for the operation state monitoring and automated fault detection of wind turbines. A five-step computation procedure has been developed in which the fault detection process is relied on statistical hypothesis tests. The null hypothesis is defined as that the wind turbine is operating in the normal condition without fault. If the null hypothesis is rejected in favour of the alternative, this indicates the occurrence of a fault in the wind turbine. The detection of a fault is indicated by an abrupt change from 0 to 1 in the test decision. The method can monitor one or several key process parameters of the wind turbine simultaneously. The monitoring process for each process parameter uses only its own data, thus, is independent with the monitoring process for other parameters. Hence, we do not need to handle the correlation between the selected parameters. Two wind turbine SCADA data sets, including three fault events, were used as case studies for testing the developed method. In both cases, various process parameters were analysed. The results show that the method can effectively monitor the operation state of wind turbines and reliably detect the anomaly (or fault) several days before its occurrence.

Suggested Citation

  • Dao, Phong B., 2022. "On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines," Applied Energy, Elsevier, vol. 318(C).
    • Handle: RePEc:eee:appene:v:318:y:2022:i:c:s0306261922005748
    DOI: 10.1016/j.apenergy.2022.119209
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    1. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    2. Yang, Wenxian & Little, Christian & Court, Richard, 2014. "S-Transform and its contribution to wind turbine condition monitoring," Renewable Energy, Elsevier, vol. 62(C), pages 137-146.
    3. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
    4. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    5. Meyer, Angela, 2021. "Multi-target normal behaviour models for wind farm condition monitoring," Applied Energy, Elsevier, vol. 300(C).
    6. Yang, Wenxian & Court, Richard & Jiang, Jiesheng, 2013. "Wind turbine condition monitoring by the approach of SCADA data analysis," Renewable Energy, Elsevier, vol. 53(C), pages 365-376.
    7. Castellani, Francesco & Astolfi, Davide & Sdringola, Paolo & Proietti, Stefania & Terzi, Ludovico, 2017. "Analyzing wind turbine directional behavior: SCADA data mining techniques for efficiency and power assessment," Applied Energy, Elsevier, vol. 185(P2), pages 1076-1086.
    8. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    9. Xiang, Ling & Yang, Xin & Hu, Aijun & Su, Hao & Wang, Penghe, 2022. "Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks," Applied Energy, Elsevier, vol. 305(C).
    10. Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    11. Qu, Fuming & Liu, Jinhai & Zhu, Hongfei & Zhou, Bowen, 2020. "Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic," Applied Energy, Elsevier, vol. 262(C).
    12. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    13. 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.
    14. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
    15. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    16. 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.
    17. Gonzalez, Elena & Stephen, Bruce & Infield, David & Melero, Julio J., 2019. "Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study," Renewable Energy, Elsevier, vol. 131(C), pages 841-853.
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