Publications Internationales

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Author: search.filters.author.Mammeri, Elhachemi

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    New MPPT hybrid controller based on genetic algorithms and particle swarm optimization for photovoltaic systems
    (2023) Mammeri, Elhachemi; Ahriche, A.; Necaibia, A.; Bouraiou, A.
    Traditional Maximum Power Point Tracking (MPPT) techniques are unable to reach high performance in photovoltaic (PV) system under partial shading conditions because of the multi-peaks present in the Power-Voltage curve. For that, particle Swarm Optimization (PSO) and genetic algorithms (GA) have been combined in recent years. However, these algorithms demonstrate some drawbacks in tracking accuracy and convergence rates, which impair control performance. In this paper, a new controller based on hybridization of PSO and GA is introduced to track the global maximum power point (GMPP). The proposed algorithm (HPGA) increases the balance rate between exploration and exploitation due to the cascade design of GA and PSO. Thus, the GMPP tracking of both algorithms will be improved. Simulations are carried out based on ISOFOTON-75W PV modules to prove the high performance of the proposed algorithm. From the obtained results, we conclude that HPGA shows fast convergence and very good tracking accuracy of GMPP in PV system even under different shading patterns.
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    An enhanced battery model using a hybrid genetic algorithm and particle swarm optimization
    (Springer Nature, 2023) Mammeri, Elhachemi; Ahriche, Aimad; Necaibia, Ammar; Bouraiou, Ahmed; Mekhilef, Saad; Dabou, Rachid; Ziane, Abderrezzaq
    Batteries are widely used for energy storage in stand-alone PV systems. However, both PV modules and batteries exhibit nonlinear behavior. Therefore, battery modeling is an essential step toward appropriate battery control and overall PV system management. Empirical models remain reliable for lead-acid batteries, especially the Copetti model, which describes many inner and outer battery phenomena, including temperature dependency. However, the parameters of the Copetti model require further adjustment to increase its ability to accurately represent battery behavior. Recently, metaheuristic algorithms have been employed for parameter identification, especially hybrid algorithms that combine the advantages of two or more algorithms. This paper proposes an enhanced battery model based on the Copetti model. The parameter identification of the enhanced model has been carried out using a novel hybrid PSO-GA algorithm (HPGA). The hybrid algorithm combines GA and PSO in a cascade configuration, with GA as the master algorithm. The HPGA algorithm has been compared with other algorithms, namely GA, PSO, ABC, COA, and a hybrid GWO-COA, to reveal its advantages and disadvantages. The NRMSE is used to evaluate algorithms in terms of tracking speed and efficiency. HPGA shows an improvement in tracking efficiency compared to GA and PSO. The proposed model is validated on several charging-discharging data and exhibits a 15% lower mean error compared to the Copetti model with original parameters. Additionally, the proposed model demonstrates a lower mean error of 0.16% compared to other models in the literature with a 0.36% mean error at least.