Reliable Multi-Target SCADA-Based Condition Monitoring for Wind Turbine Pitch Fault Detection

Abstract

Wind energy plays a key role in modern power systems, but the rapid expansion of the sector creates challenges for the long-term reliability and operational safety of wind turbines. Normal behavior models (NBMs) are widely used for turbine-performance monitoring, yet most existing approaches rely on single-target formulations that increase deployment complexity and often produce ambiguous fault indications. To address these limitations, we propose a multi-target NBM that jointly monitors multiple supervisory control and data acquisition (SCADA) signals through a unified health indicator. The framework integrates autoregressive (AR) lag features within a stacking ensemble to capture temporal dependencies and nonlinear operating patterns, and couples the resulting residuals with a LOESS-smoothed mean-squared-deviation (MSD) decision statistic. The proposed framework is then validated on pitch-fault anomaly data using active power and gearbox-oil temperature signals. Compared with conventional single-target NBMs, the proposed approach suppresses spurious alerts, increases confidence in detected anomalies, reduces monitoring effort, preserves predictive accuracy, and improves the reliability of fault diagnosis.