Fuzzy logic approach based mppt for the dynamic performance improvement for PV systems

Renewable Energies (RE) are considered as an important alternative sources of energy for the generation of electricity such as hydrogen and photovoltaic energies. To ensure an efficient photovoltaic energy conversion several Maximum Power Point Tracking (MPPT) algorithms have been developed to incite the PV field to deliver maximum power. This paper presents a comprehensive comparative study of two classical MPPT algorithms against the Artificial Intelligence (AI)-based one under hypothetical and realistic atmospheric conditions. The suggested algorithms are Perturb and Observe (P&O) algorithm, Incremental of Conductance (IC) algorithm and Fuzzy Logic based Incremental Conductance (FL-IC). After the PV verification under hypothetical conditions such as slow and fast irradiance variations, the effectiveness of those numerous methods are evaluated in terms of stability, robustness and rapidity considering one-year realistic atmospheric irradiance data for Bouzareah region. Through MatlabTM-simulation results, the superiority of the FL-IC over other both classical algorithms is highlighted.

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