FLC based Gaussian membership functions tuned by PSO and GA for MPPT of photovoltaic system: A comparative study

• Published in: International Conference on Systems and Control (Print) pp. 317 - 322
• Main Authors: Bouarroudj, Noureddine, Boukhetala, Djamel, Djari, Abdelhamid, Rais, Younes, Benlahbib, Boualem
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2017
• Subjects: Batteries; Genetic algorithms; Mathematical model; Maximum power point trackers; Niobium; Photovoltaic systems
• ISSN: 2379-0067
• DOI: 10.1109/ICoSC.2017.7958640

This article focuses on two principal subjects. In the first, a Mamdani Fuzzy Logic Controller (FLC) based Gaussian membership functions is considered for the maximum power point tracking (MPPT) of a photovoltaic (PV) system. In the second, to improve the performance of the system in the feedforward loop, we used the particle swarm optimization algorithm (PSO) and Genetic Algorithm (GA) to tune the parameters of the FLC, such as gains of normalization, deviations and means of membership functions. The optimization of FLC by PSO and GA is done under the standard climatic condition (T = 25 o C, and S = 1000 W/m 2 ). The robustness and effectiveness of the optimized FLCs are carried out under a variable climatic condition of temperature and irradiance. Finally, the simulation results show that the optimized FLCs give better performance compared with FLC not optimized, and classical Perturbation and Observation (P&O) algorithm.