Physics-Informed Dynamic Maintenance Optimization for Offshore Wind Farms Under Hybrid Uncertainty: A Distributionally Robust Approach

Abstract

This study proposes a multistage maintenance optimization framework for offshore wind farms under stochastic and fuzzy uncertainty. Unlike purely statistical approaches, it embeds physical degradation mechanisms—Miner’s fatigue rule, Arrhenius thermal aging, and a power-law corrosion model—directly into a Markov Decision Process, preserving physical traceability. Component failures follow physics-calibrated Weibull distributions, while epistemic maintenance-cost uncertainty is handled via a 1-Wasserstein distributionally robust optimization framework that guarantees performance without exact probability assumptions. A multiobjective model minimizes worst-case cost, maximizes reliability, and minimizes downtime, solved by a two-layer exact method combining AUGMECON2 for Pareto-front generation with an inner mixed-integer linear programming reformulation. Validated on synthetic data calibrated to Taiwan Strait conditions, the framework consistently outperforms fuzzy goal programming, standalone MDP, and genetic algorithm benchmarks, confirming the value of physics-informed, distributionally robust operation and maintenance decision-making.