An approach combining data mining and control charts-based model for fault detection in wind turbines

Abstract

Wind energy is growing to be one of main sources of renewable energy. As the operational and maintenance costs of wind turbines are adversely affected by the occurrence of faults, the early detection of potential faults can help reduce such costs. In this study, we propose a method for detecting potential faults sooner and identifying the probable variables contributing to the faults over a certain period as well as at a specific time. The proposed method uses data mining techniques to select the more important variables from the supervisory control and data acquisition (SCADA) systems of the turbine to improve the prediction accuracy and employs an exponentially weighted moving average (EWMA) model-based control chart to implement the residual approach, in order to remove the autocorrelation in the data. Both EWMA and multivariate EWMA (MEWMA) control charts are constructed so that their detection capabilities as well as the types of errors generated can be compared. We evaluated the proposed method by using both the SCADA data and the alarm log of a turbine. It was observed that the MEWMA chart is more suitable than the EWMA chart for the early detection and avoidance of errors.