Title :
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
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;
Conference_Titel :
Sustainable Power Generation and Supply, 2009. SUPERGEN '09. International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4934-7
DOI :
10.1109/SUPERGEN.2009.5348129
