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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Applied Power Engineering (IJAPE) Journal of Robotics and Control (JRC)
Benydir, Mohamed
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Hybrid MPPT Control: P&O and Neural Network for Wind Energy Conversion System Dahmane, Kaoutar; Boulaoutaq, El Mahfoud; Bouachrine, Brahim; Ajaamoum, Mohamed; Imodane, Belkasem; Mouslim, Sana; Benydir, Mohamed
Journal of Robotics and Control (JRC) Vol 4, No 1 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i1.16770

Abstract

In the field of wind turbine performance optimization, many techniques are employed to track the maximum power point (MPPT), one of the most commonly used MPPT algorithms is the perturb and observe technique (PO) because of its ease of implementation. However, the main disadvantage of this method is the lack of accuracy due to fluctuations around the maximum power point. In contrast, MPPT control employing neural networks proved to be an effective solution, in terms of accuracy. The contribution of this work is to propose a hybrid maximum power point tracking control using two types of MPPT control: neural network control (NNC) and the perturbation and observe method (PO), thus the PO method can offer better performance. Furthermore, this study aims to provide a comparison of the hybrid method with each algorithm ???????? and NNC. At the resulting duty cycle of the 2 methods, we applied the combination operation. A DC-DC boost converter is subjected to the hybrid MPPT control.  This converter is part of a wind energy conversion system employing a permanent magnet synchronous generator (PMSG). The chain is modeled using MATLAB/Simulink software. The effectiveness of the controller is tested at varying wind speeds. In terms of the Integral time absolute error (ITAE), using the PO technique, the ITAE is 9.72. But, if we apply the suggested technique, it is smaller at 4.55. The corresponding simulation results show that the proposed hybrid method performs best compared to the PO method. Simulation results ensure the performance of the proposed hybrid MPPT control. 
Dynamic voltage restoration using neural networks for grid-connected wind turbine Dahmane, Kaoutar; Bouachrine, Brahim; Imodane, Belkasem; Idrissi, Abdellah El; Benydir, Mohamed; Ajaamoum, Mohamed; Oubella, M'hand
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5018-5029

Abstract

Wind energy is being integrated into the grid as a renewable energy source to meet the world's electricity needs. Grid-connected wind turbines are often disrupted by grid fault problems. Fault ride-through (FRT) ability has become the most important grid connection necessity for wind energy conversion systems (WECS). In the event of a voltage dip fault, the low voltage ride-through (LVRT) capacity is an imperative key to successful grid integration. This paper proposes a dynamic voltage restorer (DVR) controlled through an artificial neural network (ANN) to improve the LVRT capability of a grid-connected wind turbine (WT) based permanent magnet synchronous generator (PMSG). The DVR injects series voltage into the system through a series-connected transformer. The DVR can then restore the voltage to the pre-fault value. The injection transformer is connected to the line linking the PMSG-based wind turbine output to the utility grid. Design and simulation of the low voltage ride-through applied to symmetrical and asymmetrical fault conditions were performed in MATLAB/Simulink software. Simulation results approve that the performance of the technique fully demonstrates its effectiveness and practicality.
Optimization and management of solar and wind production for standalone microgrid: a Moroccan case study El Hafydy, Mohamed; Oubail, Youssef; Benydir, Mohamed; Elmahni, Lahoussine; Alaoui My Rachid, Elmoutawakil
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i1.pp202-211

Abstract

The increasing demand for sustainable and efficient energy solutions has prompted extensive research into optimizing renewable energy sources in microgrid systems. This paper focuses on optimizing renewable energy sources within a standalone microgrid using particle swarm optimization (PSO) as the sole algorithm. The microgrid model proposed integrates photovoltaic (PV), wind, battery storage, and serves a load represented by an agricultural firm. Real-world data from Agdz in Ouarzazate, Morocco, is utilized for analysis. The primary objective is to minimize excess production from PV and wind sources when the battery reaches full charge. This research addresses the increasing demand for sustainable energy solutions by emphasizing a single optimization technique, PSO, for achieving a balanced and efficient energy generation system. The study aims to closely align energy production with load demand to reduce wastage and ensure a reliable energy supply within the microgrid. The evaluation is conducted based on the ability of the PSO algorithm to diminish the gap between total energy production and load demand. The use of the PSO algorithm resulted in a 30% reduction in excess energy, effectively mitigating unnecessary energy wastage when the battery is fully charged. This outcome highlights the algorithm's capacity to adapt and optimize energy production from primary sources to precisely align with the specific requirements of the load
Experimental validation of two voltage regulation strategies for boost converters in wind systems Imodane, Belkasem; Benydir, Mohamed; Bouachrine, Brahim; Ajaamoum, Mohamed; Dahmane, Kaoutar
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i1.pp509-518

Abstract

This study provides an experimental validation of two advanced control methods, sliding mode control (SMC) and fuzzy logic control (FLC) for regulating the DC bus voltage in a permanent magnet synchronous generator (PMSG) wind turbine system using a boost converter. Initially, MATLAB/Simulink simulations are used to assess the system's behavior in an ideal environment, where various operating conditions and disturbances are modeled to test the robustness of the control algorithms. Subsequently, real experiments are conducted using a physical prototype of a boost converter and a LAUNCHXL-F28069M DSP board to evaluate the system's behavior under real-world scenarios. The evaluation focuses on system stability, tracking accuracy, and response time under various wind turbine operating conditions. The experimental results reveal that SMC outperforms FLC in terms of rapidity, precision, and hardware implementation. Additionally, SMC offers significant advantages in achieving superior performance metrics, such as improved dynamic response and enhanced overall system stability, making it a more effective choice for practical wind energy applications. This experimental validation simplifies the selection of optimal control strategies for wind energy systems.
Comparative analysis of MPPT techniques for photovoltaic systems: classical, fuzzy logic, and sliding mode approaches hafydy, Mohamed El; Benydir, Mohamed; Lahoussine, Elmahni; My Rachid, Elmoutawakil Alaoui; Oubail, Youssef
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i3.pp688-700

Abstract

This study presents a comprehensive comparative analysis of maximum power point tracking (MPPT) strategies for photovoltaic systems, focusing on the classical perturb and observe (P&O) method, an artificial intelligence based fuzzy logic controller (FLC), and a robust sliding mode control (SMC) technique. These methods aim to maximize power output by dynamically adapting to rapid and unpredictable environmental variations, such as changes in solar irradiance. Simulations performed the MATLAB/Simulink environment under diverse real-world scenarios demonstrate that SMC and FLC outperform the conventional P&O approach, particularly under conditions of sudden and severe environmental in fluctuations. The findings highlight the advanced controllers’ ability to sustain optimal power extraction, minimize energy losses, and maintain system stability across varying operating conditions. These results underscore the potential of SMC-based MPPT systems to enhance the efficiency and resilience of renewable energy applications, making them highly viable for deployment in real-world scenarios characterized by volatile environmental conditions.
Co-Authors Ajaamoum, Mohamed Alaoui My Rachid, Elmoutawakil Bouachrine, Brahim Boulaoutaq, El Mahfoud Dahmane, Kaoutar El Hafydy, Mohamed Elmahni, Lahoussine hafydy, Mohamed El Idrissi, Abdellah El Imodane, Belkasem Lahoussine, Elmahni Mouslim, Sana My Rachid, Elmoutawakil Alaoui Oubail, Youssef Oubella, M'hand