Physics-Guided Bayesian Neural Networks for Reliable Fault Detection in Wind Energy Systems

Operators of new wind farms face a catch-22: they need AI to predict failures, but AI needs years of failure data to learn—a challenge known as the "cold start" dilemma. Traditional Deep Learning is ineffective during early operations because it cannot distinguish between transient weather anomalies (like sudden wind gusts) and mechanical degradation without thousands of training examples.To overcome this, this paper proposes a Physics-Guided Bayesian Neural Network (Pg-BNN). By embedding the turbine's aerodynamic power curve directly into the neural network's loss function, the model is constrained by physical laws, effectively penalizing physically impossible predictions. A Bayesian layer is integrated to quantify epistemic uncertainty, allowing the system to output a confidence score that suppresses false alarms during unprecedented weather events. Furthermore, the model's probabilistic fault outputs are coupled with an Ant Colony Optimization (ACO) algorithm to dynamically schedule and route repair crews based on failure urgency. Evaluated on the NREL 5MW reference dataset, the Pg-BNN reduced required training data from over 85 days to just 14 days, boosting early fault detection accuracy to 91.8%. This architecture provides a robust, physics-aware safety net for early-stage wind farm operations.