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PREDICTIVE ANALYSIS OF WIND TURBINE FAULTS: A DATA MINING APPROACH

Wind industry is expanding rapidly to meet the current energy challenges. The expansion in quantity and size of the wind turbines will also increase the operation and maintenance (O&M) cost. Monitoring the performance of wind turbines can reduce the O&M cost. Supervisory Control and Data Acquisition (SCADA) system records various wind turbine parameters that can be analyzed for performance monitoring. Data mining provides an easy yet robust approach to performance monitoring by analyzing historical data. In the research reported in this paper, data mining techniques are investigated to monitor faults related wind turbines generators and blades. Data mining algorithms namely boosting tree (BT), support vector machine (SVM), k-nearest neighbor (k-NN), and Genetic programming (GP) are employed to build prediction models.