An ANFIS-based Modeling for a Photovoltaic Power Supply (PVPS) System

Abstract

Due to the increasing need for intelligent systems, the Adaptive Neuro-fuzzy Inference System (ANFIS) has recently attracted the attention of researchers in various scientific and engineering areas. The purpose of this work is to present the modeling of a Photovoltaic Power Supply PVPS-system using an ANFIS. For the modeling of the PVPS-system, it is required to find suitable models for its different components (ANFIS-PV-array, ANFIS-battery and ANFIS-regulator) under variable climatic conditions. A database of measured weather data (global radiation and temperature) and electrical signals (photovoltaic, battery and regulator voltage and current) of a PVPS system installed in Tahifet (south of Algeria) has been recorded for the period from 1992 to 1997 using a data acquisition system. These data have been used for the modeling and simulation of the PVPS-system. The ANFIS for the PV-array, battery and regulator have been trained by using 8 signals recorded from the different components of the PVPS system. Each signal is represented by 365*5 values (complete 5-years). A set of data for 4-years have been used for the training of the ANFIS, and data for 1-year has been used for the testing of the ANFIS. In this way, the ANFIS was trained to accept and handle a number of unusual cases. The comparison between actual and estimated values obtained from the ANFIS gave satisfactory results. The correlation coefficient between measured values and those estimated by the ANFIS gave good prediction accuracy of 98%. In addition, test results show that the ANFIS performed better than the neural networks. The results obtained from ANFIS can also be used for the prediction of the optimal configuration of PV systems, for the control of PV systems and for the prediction of the performance of the systems.