Performance Evaluation of Grid-Connected Solar Photovoltaic (SPV) System with Different MPPT Controllers

Abstract

Renewable energy plays an important role in electric power generation. Solar energy is one of them. It has the advantage of no pollution, low maintenance cost, no installation area limitation, and no noise due to the absence of the moving parts. However, high initial cost and low conversion efficiency have deterred its popularity. Due to the non-linear relation between the voltage and current of the PV cell, it is observed that there is unique Maximum Power Point (MPP) at particular environmental conditions, and this peak power point keeps changing with solar irradiations and ambient temperature. To achieve high efficiency in SPV power generation it is required to match the SPV source and load impedance properly for any weather conditions, thus obtaining maximum power generation. The technique process of MPP is being tracking which is called Maximum Power Point Tracking (MPPT). In recent years, a large number of techniques have been proposed for MPPT and some based on Computational Intelligence (CI) techniques. In this chapter performance evaluation of DC–DC boost converter based on P&O and INC has been compared. The scope of the work is to first give the detailed mathematical model of grid connected three-phase SPV system. A parametric model of SPV cell is presented. Second, thermal modeling and switching loss calculation of switching devices has been discussed and then the performance evaluation will be carried out for P&O and INC based MPPT algorithms for various operating conditions of the SPV array, in terms of energy injected to grid, switching losses, junction temperature and sink temperature, for switching in the DC–DC boost converter. Application of an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) based intelligent controller has been described and applied for fast, accurate, and efficient control of DC–DC boost converter used for SPV system, in place of conventional (PI) controllers.