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A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification

Author

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  • Wang, Anqi
  • Pei, Yan
  • Qian, Zheng
  • Zareipour, Hamidreza
  • Jing, Bo
  • An, Jiayi

Abstract

Effective condition monitoring is critical to improve the reliability and reduce unplanned downtime of wind turbines (WTs). Supervisory control and data acquisition (SCADA) data with the advantages of easy access and low cost, has been widely used in wind turbine condition monitoring (WTCM). While the existing literature based on SCADA data for WTCM provides whether the condition is normal or abnormal, few have attempted to identify the underlying causes of the anomaly. In this paper, a two-stage anomaly decomposition scheme based on the multi-variable correlation extraction is proposed for WT fault detection and anomaly causes identification. Firstly, a normal behavior model (NBM) based on the multi-variable correlation extraction (Co-NBM) is proposed for WT fault detection using only SCADA data. A network based on the multi-head self-attention mechanism is designed to extract correlation among the variables. It can effectively provide early warning and reduce false alarms rate by exploring the multi-variable correlations. Furthermore, a two-stage anomaly decomposition scheme based on the Hotelling’s T2 method is proposed for WT fault identification. It is capable of effectively identifying the fault locations and anomaly causes based on the defined two-stage abnormal factors. Compared with auto-encoder (AE) and LSTM-attention methods, the proposed method could achieve better performance in fault detection and reduce the false alarm rate. Moreover, the proposed method could also provide the underlying cause of the anomaly, which is useful for the decision making of WT maintenance.

Suggested Citation

  • Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(C).
    • Handle: RePEc:eee:appene:v:321:y:2022:i:c:s0306261922007164
    DOI: 10.1016/j.apenergy.2022.119373
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    References listed on IDEAS

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    Cited by:

    1. Phong B. Dao, 2023. "On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data," Energies, MDPI, vol. 16(5), pages 1-17, March.
    2. Alessandro Murgia & Robbert Verbeke & Elena Tsiporkova & Ludovico Terzi & Davide Astolfi, 2023. "Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis," Energies, MDPI, vol. 16(2), pages 1-20, January.
    3. Davide Astolfi, 2023. "Wind Turbine Drivetrain Condition Monitoring through SCADA-Collected Temperature Data: Discussion of Selected Recent Papers," Energies, MDPI, vol. 16(9), pages 1-4, April.
    4. Wang, Anqi & Pei, Yan & Zhu, Yunyi & Qian, Zheng, 2023. "Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern," Renewable Energy, Elsevier, vol. 211(C), pages 918-937.

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