Title: Artificial intelligence-based MPPT techniques for solar PV-powered PEM electrolyser system

Authors: Raj Kapur Kumar; Paulson Samuel

Addresses: Department of Electrical Engineering, MNNIT Allahabad, Prayagraj, UP, India ' Department of Electrical Engineering, MNNIT Allahabad, Prayagraj, UP, India

Abstract: Green hydrogen production is a sustainable energy solution, relying on renewable sources like solar photovoltaic (PV) systems. To ensure optimal efficiency, PV arrays must work at their maximum power point (MPP). However, traditional maximum power point tracking (MPPT) methods, such as perturb and observe (P&O), face challenges in adapting to fluctuating irradiance conditions, leading to inefficiencies in connected systems like proton exchange membrane (PEM) electrolysers. This paper presents an intelligence-driven artificial neural network (ANN) MPPT controller that deals with these issues and improves the performance of solar PV-powered PEM electrolyser systems. The ANN-based controller is designed to provide faster and more precise MPP tracking by effectively adapting to dynamic environmental conditions. Simulations conducted in MATLAB Simulink validate the proposed method. The results demonstrate that the ANN-based MPPT algorithm outperforms the conventional P&O method, offering faster computation times, higher efficiency, and stable operation even under variable irradiance. This ensures the efficient usage of solar PV energy for hydrogen generation, enhancing the complete sustainability of the structure.

Keywords: ANN-based MPPT; electrolysis of water; green hydrogen production; P&O algorithm; renewable energy.

DOI: 10.1504/IJPELEC.2026.152434

International Journal of Power Electronics, 2026 Vol.22 No.2, pp.185 - 200

Received: 03 Mar 2025
Accepted: 18 Jul 2025
Published online: 19 Mar 2026 *

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