COMPARITIVE STUDY OF TRADITIONAL AND INTELLIGENT MPPT TECHNIQUES FOR PHOTOVOLTAIC SYSTEMS

Abstract

Electricity has become a basic necessity for humans in day-to-day life to fuel all kinds of industries, agriculture, households etc. In India, major portion of electricity is generated using fossil fuels like coal, oil and natural gas. As the fossil fuels are depleting at a fast pace, there is an urgent need of switching to renewable resources for power generation. Solar energy is seen as one of the most promising renewable energy source. Presently, in India 13.5% of electricity is generated from solar energy whereas 59.1% of it is generated from fossil fuels[1]. A lot of research is going on related to Solar Photovoltaic(SPV) systems to enhance its efficiency with higher affordability which can make it much more feasible and sustainable for human needs. SPV converts the solar energy from sunlight to electrical energy using photovoltaic effect. The DC power is efficiently extracted by the solar PV array using the optimum firing of the MOSFET/IGBT switch of the converter using Maximum Power Point Tracking (MPPT) techniques. The various MPPT techniques being compared in the project are ‘Perturb & Observe Algorithm’, ‘Incremental Conductance Algorithm’, ‘Type-1 Fuzzy Logic Controller’ and Artificial neural Network. The photovoltaic array of 13.68kW of SunPower SPR-76R-BLK-U module with 45 series connected modules in 4 parallel strings is used for the present studies. The MATLAB Simulink Model for standalone SPV system using boost converter is developed and validated under uniformly varying atmospheric conditions. Comparative analysis between MPPT techniques has been carried out based on fill factor, power loss, power extracted and ripples.