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Condition monitoring of wind turbine based on incremental learning and multivariate state estimation technique

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  • Wang, Ziqi
  • Liu, Changliang
  • Yan, Feng

Abstract

With the development of wind turbine (WT) operation and maintenance technologies, the condition monitoring (CM) method based on the data of supervisory control and data acquisition (SCADA) systems has become one of the most popular and cheapest ways to detect the anomalies. As the operation condition of WT will change over time, it is necessary to real-time update the CM model with little manual maintenance to ensure its long-term performance. Therefore, a SCADA data-driven WTCM method based on incremental learning and multivariate state estimation technique (MSET) is proposed. Firstly, for the memory matrix (MM) of MSET, a similarity-based sample selection method is proposed, which is simpler and more effective than the previous method. Secondly, based on the proposed sample selection method, an incremental learning strategy for MSET is proposed, which can add the normal new data in MM and remove the redundant data in real-time. Aiming at the slow computation speed caused by the large MM, a dynamic down-sampling method is proposed, which makes only half of the data participate in the real-time computation and can reduce the computation time by at least 80%. About three years SCADA data of two different types of WTs are used to verify the proposed method. The experimental results show that the incremental MSET can maintain higher estimation accuracy and lower false alarm rate in long-term operation, and can detect the potential gearbox faults hours to weeks in advance.

Suggested Citation

  • Wang, Ziqi & Liu, Changliang & Yan, Feng, 2022. "Condition monitoring of wind turbine based on incremental learning and multivariate state estimation technique," Renewable Energy, Elsevier, vol. 184(C), pages 343-360.
    • Handle: RePEc:eee:renene:v:184:y:2022:i:c:p:343-360
    DOI: 10.1016/j.renene.2021.11.071
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    References listed on IDEAS

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