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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Applied Power Engineering (IJAPE)
Bahri, Hicham
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Control & Systems Engineering Electrical & Electronics Engineering

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Efficient and robust nonlinear control MPPT based on artificial neural network for PV system Abdouni, Khadija; Ennasri, Hind; Drighil, Asmaa; Bahri, Hicham; Bour, Mohamed; Benboukous, Mostafa
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1914-1924

Abstract

The objective of this paper is to optimize the energy generation of a photovoltaic system by proposing an improved maximum power point tracking (MPPT) technique. The proposed method combines an artificial neural network (ANN) with a backstepping controller to enhance the photovoltaic (PV) system’s efficiency and precision in diverse climatic conditions, including solar irradiance and temperature. The ANN is used to predict the optimal voltage at maximum power point (MPP) Vpv, ref, and the backstepping controller is used to control the DC/DC converter based on Vpv, ref. The results obtained using this technique are compared with those obtained from the perturbation and observation (P&O) technique. The proposed technique achieves better results than P&O in terms of efficiency, accuracy, stability, and response time. The simulations are performed on MATLAB/Simulink software.
Innovative GMPPT searching algorithm and precise backstepping control for grid-connected PV system in challenging shading environments Bahri, Mohamed; Talea, Mohamed; Bahri, Hicham; Aboulfatah, Mohamed
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1537-1546

Abstract

Photovoltaic (PV) systems encounters different problems of weather conditions that lowers their generated power. For this reason, maximum power point tracking (MPPT) have been designed to track the maximum power at all times and thus minimize these losses. However, under complexes partial shading condition (PSC) these losses are even higher. Classical MPPT algorithms fails to track the global MPP (GMPP) which further augment the power losses. Alternately, a grid connected topology of the PV system is chosen but needs a control method to phase the inverter current with the grid. This paper introduces a novel algorithm named power search algorithm (PSA) that memorizes the highest peak as it scans the PV curve then returns and locks it. Due to its simplicity, this proposed method is suitable for practical use and manages to track the GMPP with high efficiency of 99.5% and a mean response time of 0.04 s. Comparison was made with a gray wolf optimization (GWO) technique. Simulation was done in MATLAB/Simulink. Results shows that the proposed algorithm performed better than the GWO in all aspect of efficiency, tracking time and oscillations around GMPP. Also, a backstepping control was used to inject a good synchronized power to the grid.
Integral backstepping control design for enhanced stability and dynamic performance of VSC-HVDC systems Lakhdairi, Chaimaa; Benaboud, Aziza; Bahri, Hicham; Talea, Mohamed
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i2.pp255-263

Abstract

The increasing demand for efficient and reliable high-voltage direct current (HVDC) transmission systems has underscored the necessity for advanced control strategies to augment system performance. This article presents the design and implementation of an integral backstepping control approach customized for voltage source converter (VSC)-based HVDC systems. The proposed methodology primarily concentrates on tackling the inherent nonlinearities, uncertainties, and disturbances that typically impede the stability and efficiency of VSC-HVDC systems. By incorporating integral action into the backstepping control framework, two key objectives are accomplished: i) precise regulation of the direct voltage at the rectifier station and accurate control of the active power at the inverter station, and ii) effective power factor correction (PFC) at both stations within the HVDC system. These objectives contribute to robust tracking performance, enhanced dynamic stability, and improved overall system efficiency. The theoretical design has been verified through extensive numerical simulations conducted in the MATLAB/Simulink environment, showcasing the efficacy of the proposed control strategy in ensuring stability and performance under varying conditions.
ANN-based MPPT for photovoltaic systems: performance analysis and comparison with nonlinear and classical control techniques Abdouni, Khadija; Benboukous, Mostafa; Asmaa, Drighil; Bahri, Hicham; Bour, Mohamed
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2780-2791

Abstract

In photovoltaic energy systems, maximum power point tracking (MPPT) techniques are essential for optimizing power output under changing climatic conditions. Several techniques have been proposed in the literature, including classical techniques such as perturb and observe (P&O) and incremental conductance (INC), nonlinear controllers such as backstepping, and artificial intelligence-based techniques like fuzzy logic. This study compares the performance of an artificial neural network (ANN)-based MPPT approach with these nonlinear and classical MPPT techniques. It analyses the advantages and limitations of the various techniques to evaluate their performance in terms of efficiency, accuracy, and output power stability under changing climatic conditions. The study aims to help researchers select the most effective technique to improve the efficiency of photovoltaic systems. The simulation was carried out using MATLAB/Simulink. The simulation results indicated that the artificial neural network achieved better performance than the other techniques in terms of tracking speed, with an efficiency of up to 99.94%, while maintaining stable output power under changing climatic conditions. The backstepping controller also showed stable output power compared to traditional techniques. Fuzzy logic had a lower efficiency than both the artificial neural network and backstepping. Perturbation and observe and incremental conductance are easy to implement, but they showed oscillations around the maximum power point, which reduces the overall efficiency of the system.
Co-Authors Abdouni, Khadija Aboulfatah, Mohamed Asmaa, Drighil Bahri, Mohamed Benaboud, Aziza Benboukous, Mostafa Bour, Mohamed Drighil, Asmaa Ennasri, Hind Lakhdairi, Chaimaa Talea, Mohamed