Please use this identifier to cite or link to this item: http://dspace.aiub.edu:8080/jspui/handle/123456789/2375
Title: Design and Performance Analysis of Robust Adaptive Neuro-Fuzzy Inference System-Based Modified P&O Algorithm of MPPT Controller for a Solar PV System
Authors: Dafader, Arnob Chandra
Hazari, Md. Rifat
Ahmad, Shameem
Mannan, Mohammad Abdul
Issue Date: Aug-2024
Abstract: Perturb and observe (P&O) is a well-known maximum power point tracking (MPPT) algorithm that is used in solar photovoltaic (PV) systems to increase its efficiency. However, as the PV system uses solar irradiance and temperature for making electric power, the fast change of these two affects the performance of P&O and the efficiency of the PV system. Thus, the P&O algorithm fails to detect maximum power point (MPP) if temperature and irradiance change quickly. Therefore, this paper presents an adaptive neuro-fuzzy inference system (ANFIS) based P&O algorithm of MPPT controller for a solar PV system to solve the issues mentioned earlier. The utilization of the proposed ANFIS in the P&O algorithm can track the fast changes in solar irradiance and temperature to extract the maximum power from the solar PV panel. Comparative analysis has been done on MATLAB/Simulink software for both the traditional P&O and the proposed ANFIS-based P&O algorithm to show the effectiveness of the proposed MPPT controller.
URI: http://dspace.aiub.edu:8080/jspui/handle/123456789/2375
Appears in Collections:Publications From Faculty of Engineering

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