Health Status Prediction of Wind Turbine Gearbox Cluster Considering Data Distribution Discrepancy

doi:10.3901/JME.2024.18.064

Abstract

Abstract: Wind turbine operation and maintenance are becoming more and more challenging due to the increasing installed capacity of wind turbines, the lack of existing historical fault data, and its distribution discrepancy among wind turbine gearboxes. To monitor the operational status of the wind turbine gearbox for optimizing the operation & maintenance strategies, a combined method is proposed for wind turbine gearbox cluster operational state prediction, based on the long short-term memory, fuzzy synthesis, transfer learning, and dynamic weighting function. Case applications are performed by using the monitoring data from 5 wind turbines of Pandaoliang wind farm in Shanxi, China. As a result, the operational state calibration method can sensitively detect potential fault information in advance. The average accuracy of state prediction based on deep transfer learning (TL) networks is as high as 92.06% when the sample data deviating from the "wind-power" curve are removed. It demonstrated that the proposed method can accurately narrow the data discrepancy among each wind turbine gearbox, and make full use of existing monitoring data with fault characteristics to predict the operational state of other wind turbine gearboxes. Meanwhile, it has important theoretical value and engineering practical significance for wind power equipment operational state prediction.

Key words: wind turbine, gearbox, operational state prediction, fuzzy synthesis, transfer learning

CLC Number:

TK83

ZHU Yongchao, ZHU Caichao, TAN Jianjun, RAN Feng, SONG Chaosheng. Health Status Prediction of Wind Turbine Gearbox Cluster Considering Data Distribution Discrepancy[J]. Journal of Mechanical Engineering, 2024, 60(18): 64-75.

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