Optimization and Performance Improvement of Grid-Connected PV Plant Based on ANN-PSO and P&O Algorithms

Abstract

This research investigated the performance of a 5 MW PV grid-connected plant in Al Fashir City, Sudan. The research aims to improve the performance and increase the efficiency of the Al Fashir plant by identifying the maximum power point and increasing the tracking efficiency based on the algorithms developed. The PV systems benefit from MPPT approaches because they improve power output and energy delivery to the load while also extending the useful life of the PV system. The P&O algorithm performance is compared to the ANN trained by the PSO method by a set of solar radiation values. However, time response, oscillation, and stability are the three most important factors to consider when evaluating the effectiveness of any MPPT algorithm. The results show that the ANN trained by the performance of the PSO algorithm was better in time response, tracking speed, and oscillation than the P&O algorithm and could identify the new power point quickly. The results of this study will assist in resizing the PV plant and improve the operation performance and efficiency to provide affordable and reliable power accessible to the people in Al Fashir city.