Stochastic Operator Residual Learning with Conformal Calibration for Risk-Aware Wind Turbine Maintenance

Wind turbine maintenance decisions are frequently made under uncertainty, yet conventional degradation models often provide deterministic predictions that ignore the tail risks associated with unexpected component failure. We propose a stochastic operator residual learning framework with conformal calibration for risk-aware wind turbine maintenance. The proposed method learns a distribution over the residual field of governing fatigue and wear equations, rather than enforcing fixed physical laws through penalty terms, so that complex loading spectra and material heterogeneities can be captured without relying on potentially inaccurate constants. An ensemble of neural operators is then trained to separate epistemic from aleatoric uncertainty, and conformal prediction is applied to calibrate the resulting confidence intervals, yielding a remaining useful life distribution with guaranteed coverage. These calibrated distributions are subsequently passed to a risk-sensitive deep reinforcement learning agent whose objective combines power production gains, maintenance costs, and a conditional value-at-risk penalty on the remaining useful life. Furthermore, gradients from the policy update flow back through the calibration layer and the stochastic operator, which allows the uncertainty representation to be shaped by the risk profile required by the controller. The main contribution is a differentiable integration of stochastic residual learning, calibrated uncertainty quantification, and risk-constrained control within a single digital twin architecture. Simulation-based evaluation on representative turbine degradation scenarios will assess whether the framework reduces unexpected failures while preserving energy yield, and the results are expected to inform safer maintenance scheduling under uncertain operating conditions.