Physics-Guided Reinforcement Learning for Reliability-Aware Gate Driving in Renewable-to-Hydrogen High-Power Converters
: High-power renewable-to-hydrogen conversion systems impose stringent and dynamically coupled constraints on semiconductor switching behavior, thermal cycling, and electrolyzer degradation. Conventional IGBT gate driving strategies rely on fixed or heuristically tuned parameters that fail to explicitly account for nonlinear electro-thermal dynamics, parasitic interactions, and downstream electrochemical aging mechanisms under stochastic renewable input. This paper reformulates gate driving as a constrained multi-objective optimal control problem and proposes a physics-guided reinforcement learning (PGRL) framework for adaptive gate trajectory morphing in megawatt-scale hydrogen converters. A unified electro-thermal–electrochemical model is constructed to capture nonlinear switching transients, parasitic inductive–capacitive effects, junction temperature evolution, Miner-based fatigue accumulation, DC-link ripple propagation, and ripple-induced electrolyzer degradation. Physics consistency is enforced through differentiable safety projection, residual regularization against governing dynamic equations, and structured policy parameterization reflecting device topology. The learning objective simultaneously minimizes switching energy, voltage overshoot, electromagnetic stress, thermal cycling amplitude, and stack degradation rate. Case studies on a 1.2 MW PEM electrolyzer system demonstrate up to 20% reduction in peak junction temperature rise, 50% ripple suppression during renewable gust events, and extension of projected electrolyzer lifetime beyond 10,000 operating hours under uncertainty. The proposed framework establishes a cross-domain bridge between microsecond-scale semiconductor control and multi-year