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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Applied Power Engineering (IJAPE)
Belkasem Imodane
IBN ZOHR University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Adaptive proportional integral control using neural networks for secondary frequency regulation in microgrids Belkasem Imodane; Mohamed Benydir; Sana Mouslim; Abdellah El Idrissi; Mohamed Ajaamoum; Brahim Bouachrine
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2229-2237

Abstract

Microgrids with high renewable energy integration face a challenge in maintaining frequency stability due to the reduced inertia of inverter-based generation and the intermittent nature of these sources. Although primary frequency regulation using virtual synchronous generator (VSG) strategies can provide fast support, it cannot fully bring the system frequency back to its nominal value. This limitation highlights the importance of secondary frequency regulation, which is implemented using proportional integral (PI) controllers. However, fixed parameter PI regulators often fail to adapt effectively to varying loads and fluctuating renewable generation. This paper proposes an adaptive secondary control strategy for microgrids that combines offline optimization with real time learning. Grey wolf optimization (GWO) is first applied offline to determine the optimal PI gains for multiple disturbance scenarios. These datasets are then used to train an artificial neural network (ANN), which updates the PI parameters in real time to achieve adaptive performance. The proposed control is implemented in a hybrid microgrid with a diesel generator, a permanent magnet synchronous generator (PMSG) wind turbine for primary support and a fuel cell for secondary regulation. Simulation results show that the adaptive PI controller improves frequency recovery and reduces steady-state error compared to conventional fixed gain PI.
Grey wolf optimization approach to optimal backstepping control for buck converter output voltage regulation Sana Mouslim; Belkasem Imodane; Imane Outana; M’hand Oubella; El Mahfoud Boulaoutaq; Mohamed Ajaamoum
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp640-652

Abstract

DC-DC converters are essential in regulating voltage levels within DC power systems, relying on high-efficiency electronic switching devices such as MOSFETs to ensure effective power conversion. Despite their widespread use, one of the major challenges encountered in practical implementations lies in accurately tuning controller parameters particularly for nonlinear approaches such as the backstepping controller. While recent studies have demonstrated the effectiveness of particle swarm optimization (PSO) in enhancing backstepping control performance, further improvements remain possible. In this work, we propose the grey wolf optimization (GWO) algorithm as an advanced and efficient technique for the optimal tuning of backstepping controller parameters. The goal is to minimize the voltage tracking error between the reference and the output of the DC-DC buck converter, ensuring enhanced dynamic response and stability. Additionally, the proposed control strategy has been experimentally implemented and validated in a photovoltaic context, demonstrating its practical relevance and strong potential for real-world energy conversion applications.
Enhancing electrolyzer performance for hydrogen production in a solar system using a buck converter with sliding mode control Abdellah El Idrissi; Belkasem Imodane; M’hand Oubella; Hatim Ameziane; Mohamed Benydir; Kaoutar Dahmane; Driss Belkhiri; Mohamed Ajaamoum
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp69-79

Abstract

As the world increasingly turns to renewable energy, green hydrogen produced through water electrolysis has emerged as a clean and promising alternative to fossil fuels. In this work, we explore a solar-powered hydrogen production system that uses real data from an operational photovoltaic (PV) installation, ensuring accurate and realistic modeling of environmental conditions. A DC-DC buck converter is used to regulate the fluctuating PV output, supplying the precise voltage needed by a PEM electrolyzer. Sliding mode control (SMC) strategy is applied to maintain voltage stability, and its performance is compared with a traditional proportional-integral (PI) controller. Simulations in MATLAB/Simulink demonstrate that SMC offers better dynamic performance, including minimal overshoot, faster response, and an impressive hydrogen production rate of 0.98 L/min (98% efficiency). By providing more consistent voltage to the electrolyzer, SMC significantly boosts overall system performance. These findings underline the potential of advanced control strategies, supported by real-world data, to make renewable hydrogen production more reliable and efficient.
Co-Authors Abdellah El Idrissi Brahim Bouachrine Driss Belkhiri El Mahfoud Boulaoutaq Hatim Ameziane Imane Outana Kaoutar Dahmane Mohamed Ajaamoum Mohamed Benydir M’hand Oubella Sana Mouslim Sana Mouslim