Design of three-phase photovoltaic grid connected inverter based on RBF neural network

Author

He su-fen ; Yi Ling-Zhi ; Li ju-cheng ; Yao zhe-zhi ; Peng han-mei

Author_Institution

Xiangtan Univ. Inf. Eng. Inst., Xiangtan, China

fYear

2009

fDate

6-7 April 2009

Firstpage

1

Lastpage

5

Abstract

A new complete RBF neural network SVPWM controller scheme for a grid-connected inverter is presented in this paper, RBF network architecture and the parameter is determined by using the clustering method, the RBF neural network which is simplifies and realize voltage space vector modulate to control the inverter. It was used in three-phase photovoltaic grid-connected inverse system which adopts control of predictive current. A computer simulation program is developed using MATLAB. The simulation results show that the method features simplicity, high efficiency and excellent control effect.

Keywords

electric current control; invertors; neurocontrollers; photovoltaic power systems; power system control; predictive control; radial basis function networks; RBF network architecture; RBF neural network SVPWM controller; clustering method; predictive current control; three-phase photovoltaic grid connected inverter; voltage space vector; Clustering methods; Computer architecture; Control systems; Inverters; Neural networks; Photovoltaic systems; Radial basis function networks; Solar power generation; Space vector pulse width modulation; Voltage control; MATLAB; MPPT; RBF Neural-Network; SVPWM; clustering method; control of predictive current; grid-connected inverter; photovoltaic system; three-phase half-bridge circuit; voltage space vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Sustainable Power Generation and Supply, 2009. SUPERGEN '09. International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4244-4934-7

Type

conf

DOI

10.1109/SUPERGEN.2009.5348129

Filename

5348129