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Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network

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  • Chen, Hansi
  • Liu, Hang
  • Chu, Xuening
  • Liu, Qingxiu
  • Xue, Deyi

Abstract

Continuous monitoring of wind turbine health conditions using anomaly detection methods can improve the reliability and reduce maintenance costs during operation of wind turbine. Anomaly detection aims at identifying the root causes leading to unexpected changes of product performance. Most existing methods make less use of temporal order of the data and are poor at extracting features from these data. To address these problems, a method based on long short-term memory (LSTM) and auto-encoder (AE) neural network is introduced to assess sequential condition monitoring data of the wind turbine. First, a performance assessment model is constructed using LSTM neural units and AE networks to calculate the performance indices for evaluation of the degree of anomalies in wind turbine performance. Then, an adaptive threshold estimation method based on support vector regression model is developed to identify the abnormal data instances. The mutual information theory is subsequently explored to analyze the relationships between various monitoring parameters and performance abnormal instances to identify critical condition monitoring parameters. The effectiveness of the proposed method has been verified by a case study using real-world wind turbine condition monitoring (CM) data.

Suggested Citation

  • Chen, Hansi & Liu, Hang & Chu, Xuening & Liu, Qingxiu & Xue, Deyi, 2021. "Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network," Renewable Energy, Elsevier, vol. 172(C), pages 829-840.
    • Handle: RePEc:eee:renene:v:172:y:2021:i:c:p:829-840
    DOI: 10.1016/j.renene.2021.03.078
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    Cited by:

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    6. Zheng, Minglei & Man, Junfeng & Wang, Dian & Chen, Yanan & Li, Qianqian & Liu, Yong, 2023. "Semi-supervised multivariate time series anomaly detection for wind turbines using generator SCADA data," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    7. Mohammad Barooni & Turaj Ashuri & Deniz Velioglu Sogut & Stephen Wood & Shiva Ghaderpour Taleghani, 2022. "Floating Offshore Wind Turbines: Current Status and Future Prospects," Energies, MDPI, vol. 16(1), pages 1-28, December.
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    10. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
    11. Zeqing Yang & Wenbo Zhang & Wei Cui & Lingxiao Gao & Yingshu Chen & Qiang Wei & Libing Liu, 2022. "Abnormal Detection for Running State of Linear Motor Feeding System Based on Deep Neural Networks," Energies, MDPI, vol. 15(15), pages 1-22, August.
    12. Urmeneta, Jon & Izquierdo, Juan & Leturiondo, Urko, 2023. "A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines," Renewable Energy, Elsevier, vol. 205(C), pages 281-292.
    13. Wang, Han & Zhang, Ning & Du, Ershun & Yan, Jie & Han, Shuang & Li, Nan & Li, Hongxia & Liu, Yongqian, 2023. "An adaptive identification method of abnormal data in wind and solar power stations," Renewable Energy, Elsevier, vol. 208(C), pages 76-93.
    14. Qian, XiaoYi & Sun, TianHe & Zhang, YuXian & Wang, BaoShi & Awad Gendeel, Mohammed Altayeb, 2023. "Wind turbine fault detection based on spatial-temporal feature and neighbor operation state," Renewable Energy, Elsevier, vol. 219(P1).
    15. Li, Xuan & Zhang, Wei, 2022. "Physics-informed deep learning model in wind turbine response prediction," Renewable Energy, Elsevier, vol. 185(C), pages 932-944.
    16. Cezary Banaszak & Andrzej Gawlik & Paweł Szcześniak & Marcin Rabe & Katarzyna Widera & Yuriy Bilan & Agnieszka Łopatka & Ewelina Gutowska, 2023. "Economic and Energy Analysis of the Construction of a Wind Farm with Infrastructure in the Baltic Sea," Energies, MDPI, vol. 16(16), pages 1-20, August.
    17. Martin Geibel & Galih Bangga, 2022. "Data Reduction and Reconstruction of Wind Turbine Wake Employing Data Driven Approaches," Energies, MDPI, vol. 15(10), pages 1-40, May.
    18. Wang, Weicheng & Chen, Jinglong & Zhang, Tianci & Liu, Zijun & Wang, Jun & Zhang, Xinwei & He, Shuilong, 2023. "An asymmetrical graph Siamese network for one-classanomaly detection of engine equipment with multi-source fusion," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    19. Feng, Chenlong & Liu, Chao & Jiang, Dongxiang, 2023. "Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning," Renewable Energy, Elsevier, vol. 206(C), pages 309-323.
    20. Fanjie Yang & Yun Zeng & Jing Qian & Youtao Li & Shihao Xie, 2023. "Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm," Energies, MDPI, vol. 16(3), pages 1-19, January.

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