Modeling of photovoltaic system with maximum power point tracking control by neural networks

Abstract

This paper presented the study, development and implementation of the maximum power point of a photovoltaic energy generator adapted by elevator converter and controlled by a maximum power point command. In order to improve photovoltaic system performance and to force the photovoltaic generator to operate at its maximum power point, the idea of the context of this paper deals with the exploitation of the technique of the artificial intelligence mechanism (neural network) certainly based on the three parts of the photovoltaic system (photovoltaic  module inputs (temperature and  solar radiation), photovoltaic module and control (MPPT)) that have been adopted within a simulation time of 24 hours.

In addition, to reach the optimal operating point regardless of variations in climatic conditions, the use of a neuron network based disturbance and observation algorithm (P&O) is put into service of the system given its reliability, its simplicity and view that at any time it can follow the desired maximum power.

The entire system is implemented in the Matlab / Simulink environment where simulation results  obtained are very promising and have shown the effectiveness and speed of neural technology that still require a learning base so to improve the performance of photovoltaic systems and exploit them in energy production, as well as this technique has proved that these results are much better in terms (of its very great precision and speed of computation) than those of the controller based on the conventional MPPT method P&O.