Abnormal state detection of wind turbines based on CNN-LSTM

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (22) : 11-17.

XIANG Ling，WANG Penghe，LI Jingxu

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Abstract

The bad operating environment of wind turbines leads to frequent gearbox failures. Therefore, it is particularly important to improve the reliability of wind turbines.This paper proposes a wind turbine state anomaly detection method based on deep learning prediction model and sample entropy. Firstly, data of wind farm data acquisition and monitoring control (SCADA) system is preprocessed, and input parameters are selected through distance correlation coefficient (DC) analysis.Then, the convolution neural network (CNN) and the long and short term neural network (LSTM) were combined to establish the logical relationship between the observation parameters and the target parameters. By combining the root mean square error (RMSE) and sample entropy, the residual temperature prediction of gear box bearing was analyzed to monitor the abnormal temperature changes of gear box bearing.Finally, the SCADA data of a wind field in north China is used for example verification. The results show that the method can accurately detect the temperature anomaly of the gearbox bearing and find the early faults of the wind turbine in advance, providing reference information for the maintenance of the wind farm staff.

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wind turbine / Data acquisition and monitoring control system / deep learning / sample entropy（SE） / anomaly detection

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XIANG Ling，WANG Penghe，LI Jingxu. Abnormal state detection of wind turbines based on CNN-LSTM[J]. Journal of Vibration and Shock, 2021, 40(22): 11-17

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