High Impact Factor : 4.396 icon | Submit Manuscript Online icon |

Soft Computing Techniques for Forecasting Issues in Grid Connected Solar Power System

Author(s):

Mayank Singh Parihar , Dr. C. V. Raman University; Manoj Kumar Jha, KTC College Korba

Keywords:

Solar Power System, Soft Computing Techniques, ANFIS

Abstract

This paper analyses the operation of an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) for solar photovoltaic (SPV) energy generation system. The MPPT works on the principle of adjusting the voltage of the SPV modules by changing the duty ratio of the Quasi-z-source inverter. The duty ratio of the inverter is calculated for a given solar irradiance and temperature condition by a closed-loop control scheme. The closed-loop control of the qZSI regulates the duty ratio and the modulation index to effectively control the injected power and maintain the stringent voltage, current, and frequency conditions. The ANFIS is trained to generate maximum power corresponding to the given solar irradiance level and temperature. The response of the ANFIS-based control system is highly precise and offers an extremely fast response. The main objective for a grid-connected Photovoltaic (PV) inverter is to feed the harvested energy from PV panels to the grid with high efficiency and power quality.

Other Details

Paper ID: IJSRDV7I100163
Published in: Volume : 7, Issue : 10
Publication Date: 01/01/2020
Page(s): 329-338

Article Preview

Download Article