Study on the application of Gaussian mixture model in the prognostic and health management of wind turbine
Journal
Journal of Taiwan Society of Naval Architects and Marine Engineers
Journal Volume
36
Journal Issue
2
Pages
83-92
Date Issued
2017
Author(s)
Abstract
A prognostic and health management method based on the Gaussian mixture model was proposed to analyze and predict the performance of a 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 of operating performance of wind turbines by GMM (Gaussian Mixture Model), indicating the operating performance by the CV (Confidence Value), and predicting the future CV by regression analysis. The proposed method was applied to analyze the performance data of the Wind Turbine No.4 of Taipower 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 decaying 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 unhealthy at that time.
Subjects
Gaussian distribution; Health; Regression analysis; Abnormal conditions; Density based spatial clustering of applications with noise; Gaussian Mixture Model; Gmm (gaussian mixture model); Operating performance; Prognostic and health management; Standard deviation; Taipower companies; Wind turbines
Type
journal article