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Anomaly detection for wind turbines based on the reconstruction of condition parameters using stacked denoising autoencoders

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  • Chen, Junsheng
  • Li, Jian
  • Chen, Weigen
  • Wang, Youyuan
  • Jiang, Tianyan

Abstract

This paper proposes an approach for detecting anomalies in a wind turbine (WT) based on multivariate analysis. Firstly, the stacked denoising autoencoders (SDAE) model with moving window and multiple noise levels is developed to reconstruct the normal operating data. The correlations among multivariable and temporal dependency inherent in each variable can be captured simultaneously with moving window processing. Both the coarse-grained and fine-grained features of input data can be learned by training with multiple noise levels. Then, the monitoring indicator is derived from the reconstruction error. The threshold value of monitoring indicator is determined by statistical analysis of the values of the monitoring indicator during normal operation. To identify the most relevant parameter related to the detected anomaly in WT, the contribution degree to which each parameter contributes to the exceedance of the threshold is calculated. Finally, the abnormal level is quantified according to the overlap between test behavior distribution and baseline condition to provide supports for operation and maintenance planning of WT. Demonstration on real SCADA data collected from a wind farm in Eastern China shows that the proposed method is effective for the anomaly detection and early warning of an actual WT.

Suggested Citation

  • Chen, Junsheng & Li, Jian & Chen, Weigen & Wang, Youyuan & Jiang, Tianyan, 2020. "Anomaly detection for wind turbines based on the reconstruction of condition parameters using stacked denoising autoencoders," Renewable Energy, Elsevier, vol. 147(P1), pages 1469-1480.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:1469-1480
    DOI: 10.1016/j.renene.2019.09.041
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    Cited by:

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    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. 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).
    4. Qiao, Yanhui & Han, Shuang & Zhang, Yajie & Liu, Yongqian & Yan, Jie, 2024. "A multivariable wind turbine power curve modeling method considering segment control differences and short-time self-dependence," Renewable Energy, Elsevier, vol. 222(C).
    5. Chen, Zhen & Zhou, Di & Zio, Enrico & Xia, Tangbin & Pan, Ershun, 2023. "Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    6. Panagiotis Korkos & Jaakko Kleemola & Matti Linjama & Arto Lehtovaara, 2022. "Representation Learning for Detecting the Faults in a Wind Turbine Hydraulic Pitch System Using Deep Learning," Energies, MDPI, vol. 15(24), pages 1-17, December.
    7. 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.
    8. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Junshuai Yan & Yongqian Liu & Xiaoying Ren, 2023. "An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm," Energies, MDPI, vol. 16(10), pages 1-23, May.

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