Drift-aware deep learning and physics-informed monitoring for coupled photovoltaic-hydrogen systems

Coupled photovoltaic-hydrogen (PV–H 2 ) plants are emerging as a cornerstone technology for large-scale green hydrogen production, and their reliable, safety-compliant operation is a prerequisite for the industrial deployment of renewable hydrogen at scale. However, their monitoring remains intrinsically challenging due to multi-scale non-stationarity, cross-domain electro-electrochemical coupling, and progressive degradation phenomena. Fast regime shifts arise from irradiance intermittency and energy management strategies, whereas slow drift is induced by PV soiling, interconnect aging, membrane thinning, and catalyst degradation. Although extensive research exists on photovoltaic fault detection and electrolyzer diagnostics independently, most approaches assume quasi-stationary distributions, neglect sequential drift theory, or omit uncertainty and deployment-oriented metrics. This work proposes a unified monitoring blueprint grounded in: (i) sequential drift detection theory, (ii) forecasting-based residual amplification of progressive degradation, (iii) physics-informed regularization of PV and PEM (Proton Exchange Membrane) governing equations, (iv) uncertainty-aware decision risk modeling, and (v) deployment-driven evaluation criteria. Analytical expressions are derived for detection delay under progressive drift, operational risk is formalized under miscalibration, and a reproducible hybrid physics–data benchmark including confounder stress tests is defined. The proposed framework bridges statistical detection theory, deep multi-task learning, physics-constrained modeling and industrial hydrogen safety requirements. It establishes a structured path from theoretical analysis to the large-scale deployment of PV–H 2 plants. In practice, the derived scaling law and the accompanying benchmark are intended to translate into deployment-oriented guarantees (quantifiable detection delay, controlled false-alarm rate, and a formalized (rather than yet fully achieved) operational risk model) that are of direct relevance to plant operators and to the broader significance of AI-assisted monitoring for the safe scale-up of green hydrogen infrastructure.