Open Access

ARTICLE

Physics-Guided Reinforcement Learning for Reliability-Aware Gate Driving in Renewable-to-Hydrogen High-Power Converters

Yaohan Wang¹, Kai Liang¹, Hai Wang², Xuejiao Fu¹, Linlin Wu¹, Huaxin Zhai², Wei Li^3,*
1 State Grid Jibei Electric Power Research Institute, Beijing, China
2 State Grid Jibei Zhangjiakou Wind and Solar Energy Storage and Transportation New Energy Co., Ltd., Zhangjiakou, China
3 School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China
* Corresponding Author: Wei Li. Email:

Energy Engineering https://doi.org/10.32604/ee.2026.081558

Received 04 March 2026; Accepted 16 April 2026; Published online 31 August 2026

Abstract

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 hydrogen system reliability.

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Keywords

Physics-guided reinforcement learning; IGBT gate driving; renewable-to-hydrogen converters; electro-thermal reliability modeling; multi-objective optimal control; electrolyzer degradation mitigation

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