Description

Data-driven condition monitoring reduces downtime of wind turbines and increases reliability. Wind turbine operation and maintenance (O&M) cost is a significant factor that calls for automated fault detection systems in wind turbines. In this paper, a wind turbine anomaly detection method based on a generalized feature extraction is proposed. Firstly, wind turbine (WT) attributes collected from the Supervisory Control And Data Acquisition (SCADA) system. Finally, the detection model is trained and the abnormal state is detected by the classification result 0. Experiments consists of three cases with SCADA data demonstrate the effectiveness of the proposed method and we can able to find speed of wind turbine, the early fault detection ability of the proposed approach was verified on an operational WT. The effectiveness of the proposed method is compared with standard classifiers like k-nearest neighbors (KNN), Random Forest(RF).