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Conception d’une commande MPPT optimale à base d’intelligence artificielle d’un système photovoltaïque.

Abstract : The grid connected the photovoltaic system performance is strongly affected by the environmental conditions that undergoes, such as random atmospheric variations.This thesis work aims to improve the DC / DC converter and the PV inverter controllers’ performance against brutal climatic fluctuations. Therefore, the first part of this thesis is devoted to the comparative study between the following maximum power point tracking algorithms (MPPT): (i) the algorithm of the Incremental of Conductance (IC), (ii) Fuzzy Logic (FL) and (iii) Particle Swarm Optimization algorithm (PSO). These algorithms are tested under various atmospheric conditions such as partial shading and evaluated in terms of efficiency, stability, speed and robustness. According to the simulation results, PSO is superior than IC and FL, especially during partial shading.The second part of this thesis deals with improving the efficiency of the DC / AC control system which includes an internal DC link voltage control loop (VDC) and an external control loop for direct current regulation and in quadrature (Id, Iq) provided by the PLL. Each of these two loops includes a PI controller whose gains are optimized using meta-heuristic techniques to improve the dynamic performance of the three-phase PV system connected to the network. Therefore, a comparative study is carried out for proposed meta-heuristics techniques such as: (i) whale optimization algorithm (WOA), (ii) gray wolf optimization algorithm (GWO) (iii) the Ant-Lion Optimization algorithm (ALO) and (iv) of the Moth-Flame Optimization algorithm (MFO). The results obtained, via MatlabTM-Simulink, reveal that the proposed WOA technique performance is relevant than the other studied techniques in terms of efficiency, robustness and stability which optimizes the PI controllers gains in order to obtain the best power factor and THD values.
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Submitted on : Monday, July 20, 2020 - 2:54:32 PM
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  • HAL Id : tel-02902953, version 1

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Nedjma Aouchiche. Conception d’une commande MPPT optimale à base d’intelligence artificielle d’un système photovoltaïque.. Autre. Université Bourgogne Franche-Comté, 2020. Français. ⟨NNT : 2020UBFCA001⟩. ⟨tel-02902953⟩

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