Fault Monitoring of Wind Turbine Generator Brushes: A Data-Mining Approach

Abstract

Components of wind turbines are subjected to asymmetric loads caused by variable wind conditions. Carbon brushes are critical components of the wind turbine generator. Adequately maintaining and detecting abnormalities in the carbon brushes early is essential for proper turbine performance. In this paper, data-mining algorithms are applied for early prediction of carbon brush faults. Predicting generator brush faults early enables timely maintenance or replacement of brushes. The results discussed in this paper are based on analyzing generator brush faults that occurred on 27 wind turbines. The datasets used to analyze faults were collected from the supervisory control and data acquisition (SCADA) systems installed at the wind turbines. Twenty-four data-mining models are constructed to predict faults up to 12 h before the actual fault occurs. To increase the prediction accuracy of the models discussed, a data balancing approach is used. Four data-mining algorithms were studied to evaluate the quality of the models for predicting generator brush faults. Among the selected data-mining algorithms, the boosting tree algorithm provided the best prediction results. Research limitations attributed to the available datasets are discussed.