Condition Monitoring of Wind Turbines Based on Anomaly Detection Using Deep Support Vector Data Description

Abstract Wind turbine condition monitoring is considered a key task in the wind power industry. A plethora of methodologies based on machine learning have been proposed for monitoring wind turbines, but the absence of faulty data at the amount and the variety needed still set limitations. Therefore, anomaly detection (AD) methodologies are proposed as alternatives for fault detection. Deep learning tools have been introduced in the research field of wind turbine monitoring for the purpose of higher detection accuracy. In this work, a deep learning-based anomaly detection method, the deep support vector data description (deep SVDD), is proposed for the monitoring of wind turbines. Compared to the classic SVDD anomaly detection approach, this method combines a deep network, more specifically, a convolutional neural network, with the SVDD detector in order to automatically extract effective features. To test and validate the effectiveness of the proposed method, we apply the deep SVDD method to supervisory control and data Acquisition data from a real wind turbine use case, targeting the ice detection on wind turbine blades. The experimental results show that the method can effectively detect the generation of ice on wind turbines' blades with a successful detection rate of 91.45%.