DEVELOPMENT OF INTELLIGENT MPPT CONTROLLERS FOR SOLAR PHOTOVOLTAIC SYSTEM

Abstract

The ever growing demand and socio-economic development, the conventional energy sources have almost become obsolete for nurturing this demand. Thus, opening new avenues to search for the new and renewable energy. Solar photovoltaic (PV) technology is becoming more popular because of number of advantages. These advantages include zero green house emission, low maintenance cost, least limitations with regard to site installation. However, the conversion efficiency of PV technology is very low about 15-18%. Also, the power generated from PV system is varying with meteorological parameters like solar irradiance, temperature etc. Photovoltaic is a renewable and environment friendly technique having these benefits does not solve the problem as the efficiency is dependent on various factors such as irradiance, cell temperature, dust, sunlight’s spectral density. These factors affect the power output of the SPV system, this can be overcome to a great extent using control techniques to track the maximum power, and these techniques are known as Maximum Power Point Tracking. This dissertation is an analytical study of various impressive Maximum Power Point Tracking (MPPT) algorithm using intelligent techniques such as Fuzzy Logic, ANFIS in MATLAB-Simulink module alongside the Incremental Conductance with voltage regulation. These improvised MPPT technique reduces the tracking time and also solves the various issues associated with traditional MPPT algorithms such as Perturb and Observe, Incremental Conductance. The study involves analysis of various control algorithms which in turn controls the switching of the DC-DC Boost Converter. The research work proposes various fast and adequate tracking algorithms like fuzzy logic controller and Neuro-Fuzzy controller are tested against various meteorological parameters. Respective performance indicators have been listed for the proposed MPPT. These control techniques provide user with complete flexibility to choose the inputs and their relationship (rule base). The simulated results show that the fuzzy logic and neuro-fuzzy technique controls the integrated PV module output directly and rapidly. Thus showing the effectiveness and feasibility of the proposed controllers.