Design of Improved Soft Computing based Maximum Power Point Tracking System for Operational Efficiency Enhancement of Solar Photovoltaic Energy System

Abstract

The most promising renewable energy source is solar energy, which has enormous potential but has not yet been fully investigated or converted into useful power. The process of converting solar radiation into electrical power is marked by fluctuations in output and waste. These losses are associated with the processes of conversion, transformation, and usage. Temperature, irradiance, and shade are the main operating conditions that affect how well a solar photovoltaic system performs. The efficiency with which power is converted from the panel to the load determines the operational efficiency. Charge controllers are made to convert solar photovoltaic power into electricity for an external circuit. The goal of the research is to look into efficient algorithms that can improve the solar photovoltaic energy system's operating efficiency. In order to increase the operational efficiency of solar photovoltaic systems, research is concentrated on developing maximum power point tracking systems (MPPT) under a variety of operating scenarios, including partial shade and fluctuating irradiance. The performance of the system under various operating conditions has been investigated through the simulation of an equivalent mathematical model. A unique method for charge controller duty cycle control and solar system cooling has been developed. It is based on hybridization of PV-T System and cuckoo search optimization. Simulations of the suggested system have been run under standard, complex, and changing shading pattern and operating conditions. Simulations have shown that the devised method performs better in terms of tracked power, tracking time, and tracking stability. Comparing the suggested research to heuristic approaches based on conventional and soft computing, the solar photovoltaic system's operational performance is much improved.