Study on the Application of Gaussian Mixture Model in the Prognostic and Health Management of Wind Turbine

A prognostic and health management method based on the Gaussian mixture model is proposed in this study to analyze and predict the performance of wind turbine. The proposed method includes preprocessing the raw data of wind turbines by DBCSAN (Density-Based Spatial Clustering of Applications with Noise), building the model on operating performance of wind turbines by GMM (Gaussian Mixture Model), indicating the operating performance by the CV (Confidence Value), and predicting the CV in the future by regression analysis. The proposed method was applied to analyze the performance data of the Wind Turbine No.4 of Taiwan Power Company at Linkou District. The analysis showed that the CV is between 0.4 and 0.8 in the normal condition and is smaller than 0.4 in the abnormal condition. The CV of the wind turbine is stable between 2013 and 2015. That is, the performance of this wind turbine was not degrading obviously. Furthermore, by regression analysis, the trend of CV will reduce to 0.66 which is out of 2 standard deviations below the mean of 2013 on August 8, 2033. It means that the wind turbine may be probably unhealthy at that moment.