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Khaled Bataineh
Jordan University of Science and Technology
Control & Systems Engineering Electrical & Electronics Engineering

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Adaptive Neuro-Fuzzy Inference System-based Improvement of Perturb and Observe Maximum Power Point Tracking Method for Photovoltaic Systems Khaled Bataineh; Yazan Taamneh
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 8, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (522.057 KB) | DOI: 10.11591/ijpeds.v8.i3.pp1327-1334

Abstract

This paper presents a maximum power point (MPP) tracking method based on a hybrid combination between the fuzzy logic controller (FLC) and the conventional Perturb-and-Observe (P&O) method. The proposed algorithm utilizes the FLC to initialize P&O algorithm with an initial duty cycle.  MATLAB/Simulink models consisting of, the photovoltaic system, boost converter and controllers, are built to evaluate the performance of the proposed algorithm. To accurately illustrate the performance of the proposed algorithm, comparisons with standalone FLC and P&O are carried out. The performance of the proposed algorithm is investigated difficult weather conditions including sudden changes and partial shading. The results showed that the proposed algorithm successfully reaches MPP in all scenarios.
Co-Authors Yazan Taamneh