Blended Ensemble Learning for Robust Normal Behavior Modeling of Wind Turbines

ABSTRACT The increasing scale of wind farms demands more efficient approaches to turbine monitoring and maintenance. Here, we present an innovative framework that combines enhanced kernel principal component analysis (KPCA) with ensemble learning to revolutionize normal behavior modeling (NBM) of wind turbines. By integrating random kitchen sinks (RKS) algorithm with KPCA, we achieved a 25.21% reduction in computational time while maintaining model accuracy. Our mixed ensemble approach, synthesizing LightGBM, random forest, and decision tree algorithms, demonstrated exceptional performance across diverse operational conditions, achieving R ² values of 0.9995 in primary testing. The framework reduced mean absolute error by 25.1% and mean absolute percentage error by 33.4% compared to conventional methods. Notably, when tested across three distinct operational environments, the model maintained robust performance ( R ² > 0.97), demonstrating strong generalization capability. The system automatically detects anomalies using a 0.1% threshold, enabling real‐time monitoring of 78 variables across 136,000+ operational records. This scalable approach integrates seamlessly with existing SCADA infrastructure, offering a practical solution for large‐scale wind farm management. Our findings establish a new paradigm for wind turbine monitoring, combining computational efficiency with unprecedented accuracy in normal behavior prediction.