Identification of an autonomous photovoltaic system using radial basis function (RBF) networks

Abstract

"The efficiency, stability and reliability of a photovoltaic energy are considered major factors for establishing this energy resource on the market. In this work, common maximum power point tracking technique, based on artificial neural network using the RBF (Radial basis function) has been proposed for a grid-connected PV system to maximize the output power of a PV array. The aim has also been improving the stability and reliability of a PV power conversion, with a certain value of temperature and radiation. We began this thesis with identification of dynamic systems we explored methods for identifying and understanding the complex behavior of a dynamic system. Among these methods, we examined Least Squares, Recursive Least Squares, Particle swarm optimization, fuzzy logic as well as the steps necessary for successful identification. We then presented the photovoltaic systems and their main characteristics, their different main components. Then we presented the different parameters and equations allowing the design of a photovoltaic installation for a specific site. The last part of our work addresses an in-depth study about artificial neural networks, we simulated a code that predicts the power produced by a photovoltaic system. The output power of the photovoltaic generator (GPV) depends on several climatic factors, such as irradiation and temperature. However, real-time tracking of the optimum operating point (MPP) is required to optimize system performance. In this work, we studied an intelligent modeling neural network to extract the maximum power from the PV Array. The simulation results demonstrated that our RBF network learned well, confirming this by the test values which gave very approximate power values or almost equal to the real power values produced by the solar panels. The characteristics of the PV were taken from electrical parameters of the BP SX 150S for learning the network. "