Maximum Power Point Tracking of PV System Using ANFIS Prediction and Fuzzy Logic Tracking

The maximum operating point of solar Photovoltaic (PV) panels changes with environmental conditions. Many methods have been proposed 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 proposed to analyze the experimental data to select the appropriate PV parameters that can recognize the MPP location. Short circuit current (Isc) and open circuit voltage (Voc) are selected as inputs factors instead of environmental influences. The paper demonstrates how these simple factors are necessary to locate accurate MPP under wide changes in environmental conditions. The statistical analysis is used to classify the data in appropriate fuzzy memberships. The proposed maximum power point tracking (MPPT) model depends on an Adaptive Neuro-Fuzzy Inference System (ANFIS) which is designed as a combination of the Sugeno fuzzy model and neural network. ANFIS of five layers with four fuzzy rules is used to acquire a high precision of locating Vmax with few adaptation epochs. The fuzzy logic controller (FLC) utilizes the ANFIS output voltage to track the MPP. The MPPT controller is designed to acquire high efficiency with low