Performance Analysis and Anomaly Detection in Wind Turbines based on Neural Networks and Principal Component Analysis

This paper proposes an approach for maintenancemanagement of wind turbines based on their life. The proposedapproach uses performance analysis and anomaly detection(PAAD) which can detect anomalies and point out the originof the detected anomalies. This PAAD algorithm utilizes neuralnetwork (NN) technique in order to detect anomalies in theperformance of the wind turbine (system layer), and then appliesprincipal component analysis (PCA) technique to uncover theroot of the detected anomalies (component layer). To validatethe accuracy of the proposed algorithm, SCADA data obtainedfrom online condition monitoring of a wind turbine are utilized.The results demonstrate that the proposed PAAD algorithm hasthe capability of exposing the cause of the anomalies. Reducingtime and cost of maintenance and increasing availability and inreturn profits in form of savings are some of the benefits of theproposed PAAD algorithm.QC 20170824