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Wind Turbine Operation Condition Identification Method Based on Variable Optimization Selection and Machine Learning

Effective identification of the operation condition of wind turbines is of great significance to the evaluation of wind turbine power generation performance and the fine management of wind farms. Due to the complex and diverse factors affecting wind turbine operation condition, it is very important to select the appropriate variable set to improve the identification accuracy of wind turbine operation condition. Therefore, a wind turbine operation condition identification method based on variable optimization selection and machine learning is proposed; then, taking the optimal variables under normal operation conditions as input and power as output, the normal behavior model of wind turbines based on BP double hidden layer neural network is constructed; finally, the combined method of DBSCAN clustering algorithm and probability confidence interval is used for residual analysis to realize effective identification of different operation conditions of wind turbines. The results show that the proposed method can deeply dig out the key influencing variables of the wind turbine operation condition, realize the effective identification of the healthy, sub-healthy and abnormal operation conditions, and conform to the actual operation conditions of the wind turbine.