Control of Z-Source Inverter Based PV System with MPPT Using ANFIS

Abstract

This paper presents an artificial intelligence based MPPT technique to deliver maximum power from a photo-voltaic (PV) system for three phase resistive load. Z-source inverter (ZSI) is employed in the system to boost PV module output voltage as well as inversion of voltage simultaneously in a single-stage. Here PV cell and PV module model are developed from mathematical equations. The ZSI eliminates the limitations of conventional boost converter interfaced with voltage source inverter (VSI). Simple boost control technique with sinusoidal pulse width modulation is used here for ZSI. Inductor and capacitor values are calculated for Z-source network. In the proposed system duty ratio control of ZSI is developed to control the output power of PV panel. Maximum power point tracking (MPPT) technique using Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed and trained with hybrid learning algorithm to identify parameters of Sugeno-type fuzzy inference systems using Matlab/Simulink tool box for PV module.ANFIS based MPPT is trained with various membership functions like trapezoidal, gauss and bell membership function .Comparative study is done for the complete system using both the artificial neural network (ANN) based MPPT technique and ANFIS based MPPT technique, based on their performance parameters and simulated results in MATLAB/Simulink.