Improving the Performance of Photovoltaic by Using Artificial Intelligence Optimization Techniques

Abstract

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