DocumentCode :
255735
Title :
A radial basis function Neural Networks based space-vector PWM controller for voltage-fed inverter
Author :
Yuxiang Zhan ; Yanfeng Chen ; Bo Zhang
Author_Institution :
Sch. of Electron. & Inf., South China Univ. of Technol., Guangzhou, China
fYear :
2014
fDate :
26-28 Aug. 2014
Firstpage :
1
Lastpage :
9
Abstract :
In this paper, the basic principle of space-voltage vector PWM (SVPWM) is presented. Due to back propagation neural networks (BP) based SVPWM controller have local optimization problem and lower training rate, a radial basis function neural networks (RBF) controller based SVPWM is proposed. Using Matlab/Simulink together with Neural Network Toolbox, we develop a computer simulation program for the RBF-SVPWM Inverter. The results indicate that the RBF-SVPWM Inverter generates less current harmonic distortion than BP- SVPWM Inverter and the traditional SVPWM Inverter.
Keywords :
PWM invertors; neural nets; radial basis function networks; voltage control; BPSVPWM inverter; back propagation neural networks; current harmonic distortion; optimization problem; radial basis function neural networks; space vector PWM controller; voltage fed inverter; Biological neural networks; Inverters; Radial basis function networks; Space vector pulse width modulation; Training; Vectors; radial basis function neural networks; space-vector pulse width modulation; voltage-fed inverter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Electronics and Applications (EPE'14-ECCE Europe), 2014 16th European Conference on
Conference_Location :
Lappeenranta
Type :
conf
DOI :
10.1109/EPE.2014.6910903
Filename :
6910903
Link To Document :
بازگشت