Wind turbine fault detection based on mutual information auto-encoding of SCADA data correlation

被引：0
|
作者：
Liu X.-F. [1 ]
Shi C.-Z. [1 ]
Yan R. [1 ]
Bo L. [1 ]
机构：
[1] College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing
来源：
Kongzhi yu Juece/Control and Decision | 2023年 / 38卷 / 10期
关键词：
fault detection of wind turbine; health index construction; maximum mutual information; multiple parameters correlation coupling; variational auto-encoder;
D O I：
10.13195/j.kzyjc.2021.1755
中图分类号：
学科分类号：
摘要：
Owing that the monitoring variables of the wind turbine supervisory control and data acquisition (SCADA) system are highly correlated and coupled, a wind turbine fault detection method is proposed based on multi-parameter correlation coupling and mutual information auto-encoder. The correlation matrix of multidimensional time series is established to describe the coupled relationship of SCADA data. The mutual information based variational auto-encoder is proposed for the encoding-decoding reconstruction of the correlation matrix. The wind turbine health evaluation index is constructed based on the reconstruction error of the correlation matrix, and then the early fault threshold is set through the update iteration of the exponential weighted moving average model. The proposed method is verified using the SCADA monitoring data of wind turbines in two wind farms. The results show that this method can effectively mine the internal correlation coupling information of SCADA multivariable time series, which can effectively improve the accuracy of wind turbine anomaly detection and the robustness to environmental interferences. Copyright ©2023 Control and Decision.
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页码：2953 / 2961
页数：8
相关论文
共 20 条
  • [1] Zhang X H, Zeng J C, Shi G N, Et al., Optimal decision of condition-based maintenance of wind turbines based on deterioration state-space partition, Control and Decision, 34, 9, pp. 1909-1916, (2019)
  • [2] Jiang N, Yan M, Li N., Icing detection of wind turbine blade based on the time-dimensional upsampling convolutional neural network, Control and Decision, 37, 8, pp. 2017-2025, (2022)
  • [3] Lin T, Zhao C L, Liu H P, Et al., Health assessment of wind turbine generator based on improved stacked auto-encoder, Computer Engineering & Science, 42, 3, pp. 517-522, (2020)
  • [4] Huang R Z, Tang B P, Yang Y N, Et al., Condition monitoring of wind turbine gearbox based on lstm neural network fusing scada data, Acta Energiae Solaris Sinica, 42, 1, pp. 235-239, (2021)
  • [5] Renstrom N, Bangalore P, Highcock E., System-wide anomaly detection in wind turbines using deep autoencoders, Renewable Energy, 157, pp. 647-659, (2020)
  • [6] Yang W G, Liu C, Jiang D X., An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring, Renewable Energy, 127, pp. 230-241, (2018)
  • [7] Zhao H S, Liu H H., Fault detection of wind turbine main bear based on deep learning network, Acta Energiae Solaris Sinica, 39, 3, pp. 588-595, (2018)
  • [8] Wei S R, Zhang X, Fu Y, Et al., Early fault warning and diagnosis of offshore wind DFIG based on GRA-LSTM-stacking model, Proceedings of the CSEE, 41, 7, pp. 2373-2383, (2021)
  • [9] Yang W X, Court R, Jiang J S., Wind turbine condition monitoring by the approach of SCADA data analysis, Renewable Energy, 53, pp. 365-376, (2013)
  • [10] Li B J, Yang Q Z., SCADA-data-based monitoring of failures in WTG transmission system, Northwest Hydropower, 1, pp. 116-119, (2018)
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