Real-time accurate detection of wind turbine downtime - An Irish perspective

Wind turbines are complex systems that are susceptible to frequent anomalies, faults, and abnormal behaviour. These are caused mainly due to off-nominal conditions, catastrophic events, and major failures, resulting in downtime conditions. An accurate and timely detection of downtime events provides crucial information for planning and decision-making. This study investigates the utility of wind power and wind speed as potential parameters for real-time downtime detection. Early and accurate detection of these anomalies using system outputs collected from monitoring stations is challenging and involved, especially when attempted in real-time. In this article, a real-time downtime detection framework is proposed that maps system outputs to turbine events - faults, scheduled, and unplanned maintenance - through online condition indicators. Without imposing strong distributional assumptions, using available training samples, an optimal, cost-sensitive real-time anomaly detection framework is proposed to determine whether a sample is anomalous. Considering the trade-off between misclassification errors and detection rates, detection studies are performed using wind power and speed - calibrated against available alarm classifiers - obtained from two Irish wind farms. The data cleaning and formatting for analysis was automated and subjected to classification with several levels of complexity. Recursive condition indicators (RCIs) such as Recursive Mahalanobis distance (RMD) and Recursive Residual Error (RRE) are chosen as features for classification. The real-time detection model becomes particularly useful when it is prohibitive to identify in advance the anomalies without a baseline of the system behaviour under such conditions. Case studies involving Irish wind Supervisory Control And Data Acquisition (SCADA) data demonstrate the successful application of the proposed work for early and accurate downtime detection with comparison to a reference machine learning approach.