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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) Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science
Imodane, Belkasem
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering 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.
Enhancing microgrid production through particle swarm optimization and genetic algorithm Mohamed, Benydir; Imodane, Belkasem; M’hand, Oubella; Mohamed, Ajaamoum; Brahim, Bouachrine; Abdellah, El idrissi; Najib, Abekiri; Kaoutar, Dahmane
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3644-3656

Abstract

The growing demand for sustainable and efficient energy solutions has led to research on optimizing renewable energy sources within microgrid systems. This study presents a comparative analysis of two prominent optimization techniques, particle swarm optimization (PSO) and genetic algorithm (GA), to enhance solar photovoltaic PV and wind production in microgrids. The aim is to achieve a balanced and efficient energy generation that closely matches the load demand, thereby minimizing energy wastage and ensuring a reliable energy supply. The two algorithms are employed using data representing PV and wind production, as well as load consumption, over a 24-hour period. The results are evaluated based on their ability to reduce the gap between energy production and load demand. Our findings reveal compelling insights into the performance of GA and PSO in the context of microgrid optimization. To validate the results obtained from the simulation, the PSO algorithm is implemented on an actual cart Digital Signal Processor DSP platform, using a processor-in-the-loop (PIL). This successful real-world application highlights the practical viability of utilizing PSO to improve solar PV and wind energy generation within microgrids.
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.
Neural control of DVR for wind turbine grid fault mitigation with PIL validation Dahmane, Kaoutar; Imodane, Belkasem; Mailal, Said; Bouachrine, Brahim; Ajaamoum, Mohamed; Oubella, Mhand
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp797-806

Abstract

Power quality issues that include voltage sag and swell challenge grid stability, not least for renewable energy systems such as wind turbines (WTs). Occurrence of these voltage disturbances impacts severely the performance of WT systems, compromising their fault ride-through (FRT) capabilities. This work investigates the application of an artificial neural network (ANN) as a controller mechanism for a dynamic voltage restorer, aimed at improving the FRT capabilities of a WT equipped with a permanent magnet synchronous generator. The approach includes employing series compensation to maintain the terminal voltage of the WT during fault conditions. This is performed by injecting voltage at the interface where the system connects to the grid, thus stabilizing the terminal voltage within the wind energy system. The control of the dynamic voltage restorer (DVR) is fundamental to improve the FRT capability. An ANN approach, as control technique is applied to drive the DVR. Training data used for ANN are obtained from a proportional-integral controller, and the proposed system is comprehensively modeled with MATLAB/Simulink. The proposed method demonstrates effective voltage restoration, under two fault scenarios: voltage sag and swell. Besides, the processor in-the-loop (PIL) test proves that the suggested control is practically implementable.
Co-Authors Abdellah, El idrissi Ajaamoum, Mohamed Benydir, Mohamed Bouachrine, Brahim Boulaoutaq, El Mahfoud Brahim, Bouachrine Dahmane, Kaoutar Idrissi, Abdellah El Kaoutar, Dahmane Mailal, Said Mohamed, Ajaamoum Mohamed, Benydir Mouslim, Sana M’hand, Oubella Najib, Abekiri Oubella, M'hand Oubella, Mhand