Robust Anomaly Detection for Offshore Wind Turbines: A Comparative Analysis of AESE Algorithm and Existing Techniques in SCADA Systems

Offshore wind turbines (OWTs) installed far from land have historically faced significant maintenance costs and loss of power generation resources due to system failures. As the era of artificial intelligence progresses, predictive and anomaly detection algorithms continue to mature. There is a prevalent trend of generating exception labels for time series data using self-determined thresholds and applies in anomaly detection. However, such an approach may compromise the integrity and robustness of the data. This paper aims to address these challenges by implementing a real-time sensor data Supervisory control data acquisition (SCADA) detection mechanism for offshore wind turbines using the Squeeze and Excitation (SE) block Auto Encoder (AESE) algorithm point-to-point data anomaly detection technique. Then, verify unsupervised results with the real label generated by the fault log. Comparative analysis with prevalent algorithms confirms the superiority and credibility of the AESE algorithm in this application.