A Novel Meta Heuristic Optimization Based Intelligent Maximum Power Point Tracking System for Operational Efficiency Enhancement of Hybrid Solar Photovoltaic Energy System

Abstract

Solar energy is most promising renewable energy resource with huge potential which is yet to be explored and transformed into production of power. The conversion of solar energy into electricity is characterized with dynamic performance and losses. These losses are related to the conversion process, transformation process as well as utilization process. The performance of solar photovoltaic system largely depends on operational condition i.e. temperature, irradiation and shading. The operational efficiency depends on the conversion efficiency of power generated from the panel to the load. Charge controllers are designed to transform the generated power from solar photovoltaic to the external circuit. The research is aimed to investigate effective algorithms for enhancement of operational efficiency of solar photovoltaic energy system. The research is focused on the development of maximum power point tracking system (MPPT) under diverse operating conditions such as partial shading and variable irradiance to improve operational efficiency of solar photovoltaic system. The simulation of an equivalent mathematical model has been undertaken to investigate the performance of the system under diverse operational condition. A novel approach based on hybridization of PV-T System and cuckoo search optimization has been proposed for duty cycle control of charge controllers and cooling of photovoltaic system. The proposed system has been simulated under normal, variable and complex shading pattern and operational condition. Simulations have demonstrated superior performance of the developed algorithm in terms of tracked power as well as tracking time and stability. The proposed research improves the operational performance of solar photovoltaic system significantly as compared to conventional and soft computing based heuristic approaches.