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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Applied Power Engineering (IJAPE) Indonesian Journal of Electrical Engineering and Computer Science
Mohamed Ajaamoum
Ibn Zohr University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Development of a photovoltaic characteristics generator based on mathematical models for four PV panel technologies Samia Jenkal; Mustapha Kourchi; Driss Yousfi; Ahmed Benlarabi; Mohamed Larbi Elhafyani; Mohamed Ajaamoum; Mhand Oubella
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i6.pp6101-6110

Abstract

A good study of photovoltaic system requests to have very precise current-voltage characteristic curves of photovoltaic modules for different technologies. The photovoltaic emulator provides an efficient solution to maintain the same current-voltage output of photovoltaic module. It includes three parts: the PV model, the control strategy, and the power converter. This paper provides three classes of modeling and simulation of photovoltaic arrays and presents the synthesis results of the current-voltage characteristic performances obtained by the modeling approaches. The models of the first class are based on electrical circuit model, those of the second class are based on multiple straight lines model, and for the third class, it is based on the look up table. The mathematical model is built using MATLAB/ Simulink, and an experimental bench was carried out to acquire an up-to-date and representative experimental database of four PV panels. This database is used for the validation of these models for the different PV panel technologies under Moroccan meteorological conditions. Following this comparative study, we came to a high agreement between the experimental and simulated current-voltage characteristics for the emulator under study.
Implementation of a low-cost intelligent street light system using internet of things Fatima Outferdine; Khalid Cherifi; Driss Belkhiri; Brahim Bouachrine; Mohamed Ajaamoum
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1387-1396

Abstract

In the contemporary world, science and technology are advancing swiftly to meet the growing human need for electricity. Within this framework, street lighting, serving as the most vital and ubiquitous element of urban lighting infrastructure, contributes significantly to public electricity consumption. Hence, enhancing the operational efficiency of street lamps becomes imperative to conserve energy. Nonetheless, traditional street lighting systems, being manually controlled, consuming excessive power, and entailing high installation expenses, present notable drawbacks and concerns. Leveraging the internet of things (IoT), advanced innovations automate various areas, including health monitoring, traffic management, agricultural irrigation, street lights, and classrooms. The current manual operation of street lights leads to substantial global energy waste. To address this, an integrated hardware and software solution is proposed. Practical implementation of the hardware devices employs a wireless sensor network, while the software focuses on developing an IoT application for data storage, analysis, and visualization. The proposed system enables effective monitoring of parameters such as ambient temperature, current, voltage, and energy consumption of photovoltaic street lights, which are used as an indicator of the lamp status. Using Xbee modules, a configuration by the X-CTU software is necessary to communicate between all street lights wirelessly. These Xbee modules are used as a leading technology for wireless sensor networks due to its low power and low cost. Experimental results demonstrate that the proposed system is energy-efficient and cost-effective, and further can be implemented in real street light systems.
Robust power optimization strategy for wind-driven induction machines using type-2 and type-1 fuzzy logic controllers Driss Belkhiri; Boujemaa Nassiri; Mohamed Ajaamoum
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i2.pp1313-1325

Abstract

This paper proposes a reliable power optimization strategy that maximizes the harvested power of induction machines driven by wind, taking into account variable wind turbulence and uncertain machine parameters. This work explores the challenging task of designing type-2 fuzzy logic (T2FL) and conventional type-1 fuzzy logic (T1FL) controllers for wind energy conversion systems that exhibit multiple non-linearities. T2FL controllers are proficient in tackling uncertainties and offer quicker and more precise decision-making capabilities. The proposed approach is beneficial as it is independent of accurate wind turbine parameters, wind speed data, or additional sensors. Rather, it utilizes the mechanical rotor speed and the wind turbine power as input, which corresponds to maximum power point tracking (MPPT) through the management of the rotor speed via the machine-side converter. Real data validates the scheme against classical controllers, and via a set of simulations and statistical analyses, performance metrics like steady-state error, overshoot, tracking speed, and efficiency are widely assessed. The results show that the proposed scheme, which is independent of a dedicated wind speed sensor, demonstrates superior tracking performance, lower tracking errors, such as lower RMSE/MAE, and higher energy yield, although the wind speed and the system parameters change rapidly. Overall, this design provides more robust performance to random wind speed variations, increases operational efficiency and wind turbines' service life, and is low in adding mass and cost.
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 Ahmed Benlarabi Belkasem Imodane Boujemaa Nassiri Brahim Bouachrine Driss Belkhiri Driss Yousfi El Mahfoud Boulaoutaq Fatima Outferdine Hatim Ameziane Imane Outana Kaoutar Dahmane Khalid Cherifi Mhand Oubella Mohamed Benydir Mohamed Larbi Elhafyani Mustapha Kourchi M’hand Oubella Samia Jenkal Sana Mouslim Sana Mouslim