Design and Performance Analysis of Robust Adaptive Neuro-Fuzzy Inference System-Based Modified P&O Algorithm of MPPT Controller for a Solar PV System
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.
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