Improving the Performance of Photovoltaic by Using Artificial Intelligence Optimization Techniques

Photovoltaic (PV) systems are taking a leading role as solar-based renewable energy source (RES) because of their unique advantages. This trend is being increased in the field Seawater desalination. paper presents study on Desalination Plant (SWDP) located Egypt feeding from utility network. The main challenge such non-linear system with high level variability optimum sizing SWDP proposed whole solar-powered while keeping good dynamic performance. In this article, solid electrical load analysis presented to assess optimal design fed by PV system. Moreover, maximum power point tracking controllers (MPPTCs) developed enhance performance To accomplish study, real grid connected seawater desalination plant implemented. selected producing 700 m3/day. experimental data were extracted through daily readings electricity consumption and water production meters. then introduced HOMER program suggest components based minimum net present cost. consists array, DC/AC converter, grid. Also, tackle low conversion efficiency system, three MPPTCs investigated improve Incremental Conductance conjunction artificial intelligence (AI) optimization techniques (Particle Swarm Optimization, Grey Wolf Optimization (GWO) Harris Hawks Optimization) for assessment PV. was constructed, modeled simulated MATLAB/SIMULINK. attained results methods promising extracting error improving SWDP. obtained simulation, well results, proves efficacy suggested strategy.