Maximum Power Point tracking under Different Environment Conditions for Solar Photovoltaic Panels Using ANFIS Model

Abstract

The maximum operating point of solar Photovoltaic (PV) panels changes with environmental
conditions. Many methods are used to locate and track the maximum power point (MPP) of PV cells. The
difficulties that face these methods are the rapid changes in solar radiation and the variety in cell
temperature which affects the MPP setting. External sensors are used in many approaches to measure
solar irradiation and ambient temperature to estimate the MPP as a function of data measured. In this
paper linear correlation is employed to analyse the experimental data to select the appropriate PV
parameters that can recognize the MPP location. Short circuit current (SCC) and open circuit voltage
(OCV) are selected as inputs factors instead of environmental influences. The paper demonstrates how
these simple factors are necessary to locate correct MPP under wide changes in environmental conditions.
The statistical analysis is used to classify the data in appropriate fuzzy memberships. The proposed MPP
locating model relies upon an Adaptive Neuro-Fuzzy Inference System (ANFIS) which is designed as a
combination of the concepts of Sugeno fuzzy model and neural network. ANFIS of five layers with four
fuzzy rules is proposed to acquire a high precision of MPP voltage estimation with few adaptation epochs.