SCADA-based early detection of wind turbine yaw motor current failures using Gaussian process surrogate models and Fisher's combined probabilistic alarm thresholding - Pandit, Ravi; Wang, Jianlin; Tinsley, Lawrence (Elsevier, 2026)
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.
Keywords: 4007 Control Engineering, Mechatronics and Robotics; 40 Engineering; 4010 Engineering Practice and Education; 7 Affordable and Clean Energy; Gaussian Process; Surrogate model; Wind turbine; Condition monitoring; Probabilistic testing