SCADA-based early detection of wind turbine yaw motor current failures using Gaussian process surrogate models and Fisher's combined probabilistic alarm thresholding

Abstract

The reliable operation of wind turbines is critical for generating low-carbon electricity in renewable energy systems. To maximize turbine uptime and minimize maintenance disruptions, smart condition monitoring and early fault detection strategies are essential. Yaw pitch failures, a common cause of performance degradation in wind turbines, are challenging to detect due to the complex relationship between wind speed, yaw pitch current, and grid current. This study proposes a Gaussian Process (GP) regression framework with square exponential covariance functions for early detection of yaw pitch failures in wind turbines. By analysing Supervisory Control and Data Acquisition (SCADA) data from a 2.5MW wind turbine over a six-month operational wind farm, we establish predictive models for three critical performance relationships: power curve (R² = 0.951, RMSE = 68.5kW), yaw pitch current versus wind speed (R² = 0.893, RMSE = 0.82A), and yaw pitch current versus grid current (R² = 0.908, RMSE = 0.74A). The yaw pitch current versus grid current reference curve demonstrates superior fault detection performance, identifying faults 80minutes after threshold exceedance with minimal false alarms, significantly outperforming power curve-based detection and wind speed-based detection. Fisher's combined probability test with an optimized threshold (p = 0.581) effectively balances detection sensitivity and false alarm minimization. The results demonstrate the model's ability to detect yaw pitch faults (>6A) effectively with minimal false alarms, offering a cost-effective SCADA-based solution for wind turbine condition monitoring that leverages the strong correlation (r = 0.79) between grid current and yaw pitch current.