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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Journal of Robotics and Control (JRC)
Mouslim, Sana
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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. 
Grey wolf optimization approach to optimal backstepping control for buck converter output voltage regulation Mouslim, Sana; Imodane, Belkasem; Outana, Imane; Oubella, M’hand; Boulaoutaq, El Mahfoud; Ajaamoum, Mohamed
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.
Co-Authors Ajaamoum, Mohamed Benydir, Mohamed Bouachrine, Brahim Boulaoutaq, El Mahfoud Dahmane, Kaoutar Imodane, Belkasem Oubella, M’hand Outana, Imane