On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions

Abstract

This article analyzes and compares the integration of two different maximum power point tracking (MPPT) control methods, which are tested under partial shading and fast ramp conditions. These MPPT methods are designed by Improved Particle Swarm Optimization (IPSO) and a combination technique between a Neural Network and the Perturb and Observe method (NN-P&O). These two methods are implemented and simulated for photovoltaic systems (PV), where various system responses, such as voltage and power, are obtained. The MPPT techniques were simulated using the MATLAB/Simulink environment. A comparison of the performance of the IPSO and NN-P&O algorithms is carried out to confirm the best accomplishment of the two methods in terms of speed, accuracy, and simplicity.

Keywords

• maximum power point tracking (MPPT)
• improved particle swarm optimization (IPSO)
• photovoltaic (PV)
• neural network and perturb and observe method (NN-P&O)