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Abstract This paper presents a data-driven approach for detecting anomalies in wind turbine sensors, specifically anemometers and wind vanes, and the development of a smart alerting system. The study focuses on utilizing SCADA and reanalysis (ERA5) data for accurate anomaly detection and reducing false alarms through smart change point detection algorithms. The methodology involves modeling normal behavior, detecting change points, and comparing power curves before and after these points. For anemometer anomaly detection, a three-year SCADA dataset from an offshore wind farm and a synthetic dataset is used, employing an XGBoost model and the PELT algorithm for change point detection. Wind vane anomalies are identified using a nine month dataset from seven turbines, with synthetic alterations to simulate misalignments. Results show successful detection of sudden changes in wind speed and direction, with smart alarms assisting operators in decision-making. This research enhances wind turbine condition monitoring, improving reliability and efficiency.
Badarinath et al. (Sat,) studied this question.