A Two-Stage Classification Method for Improved Fault Detection in Wind Turbines Based on SCADA Data

Fault detection is essential for the reliable operation of wind turbines. Traditional supervised methods for fault detection based on SCADA data face highly imbalanced datasets of normal and fault samples. This paper presents a two-stage detection method to address this limitation by integrating unsupervised anomaly detection or classification with supervised classification. In the first stage, the unsupervised classifier of OCSVM, together with two complementary anomaly scores, is used to flag deviations from normal operation or separate abnormal data samples from normal data samples. In the second stage, the supervised classifier of CNN is applied to the detected abnormal data samples to identify fault samples among only these samples, thus enhancing the discrimination capability between normal and abnormal conditions. Experiments on real-world SCADA data show that the introduced two-stage detection method noticeably improves fault detection compared to supervised methods, both in terms of accuracy and missed fault rates.