Enhancing the output power of solar cell system using artificial intelligence algorithms

Abstract

The main objective of research in the field of solar cell systems is to obtain the maximum output power. In this respect, artificial intelligence (AI) is considered the current icon. Hence, in the present paper perturbation & observation (P&O) and particle swarm optimization (PSO) algorithms were used to achieve the maximum power. Solar irradiance at three different regions of Egypt was measured using a new technique based on Arduino microcontroller. The obtained experimental results of the solar irradiance were inlaid to the MATLAB simulation program to study the performance of the proposed algorithms. Many improvements were carried out in P&O and PSO algorithms to harvest maximum power for long hours daily by a continuous modulation of the duty cycle. The output maximum power and the reaching time of both improved P&O and PSO are better than the traditional one and PV array, which indicates their efficiency in harvesting the maximum power and enhancing the performance of solar cell systems. The reinforcing of the PV system by P&O improved its efficiency by 98.733%, while PSO improved its efficiency by 99.968%.