Publications Scientifiques

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Author: search.filters.author.Bouraiou, Ahmed

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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.
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    Modeling the Effect of PV Module Orientation on the Encapsulant Browning Degradation Rate in Algeria Region
    (IEEE, 2022) Bouguerra, Sara; Agroui, Kamel; Kaaya, Ismail; Bouraiou, Ahmed; Yaiche, Mohamed Rédha; Mansour, Djamel Eddine
    The encapsulant browning degradation mode of photovoltaic (PV) modules is affected by ultraviolet irradiance, ambient temperature, and humidity of the installation site. In this article, the degradation rate for encapsulant browning, measured by short-circuit current degradation, is estimated for different PV module tilt and orientation angles in the Algiers region, characterized by a temperate climate. The activation energy for encapsulant browning is calculated using the acceleration factor modeling of the field-to-field degradation and physical models, in conjunction with the meteorological data of two locations in Algeria, where the same module type is fielded for 9–10 years. The tradeoff between the PV energy production and the PV module lifetime is carried out for different PV module positioning, assuming that encapsulant browning is the main failure mechanism experienced in the field. The results reveal that the orientation angle affects the PV module lifetime, where orienting the PV module from east to west increases the lifetime up to 30%, and the lifetime energy production up to 6%, due to reduced degradation. It is also presumed that although the Saharan region of Adrar receives more UV light and ambient temperatures throughout the year in Algeria, the PV modules fielded in the Mediterranean region of Algiers suffer higher degradation, which is explained by the high humidity experienced in Algiers that accelerates the encapsulant browning failure mechanism.