Title :
Research on Space Vector PWM Inverter Based on Artificial Neural Network
Author :
Zhi Yuan ; Jiaguang Cheng
Author_Institution :
Sch. of Autom., Tianjin Univ. of Technol., Tianjin, China
Abstract :
This paper proposed a Space Vector PWM algorithm based on artificial neural network for voltage-source inverters. When calculating the invert´s three-phase turn-on time, the paper uses a three-layer forward-feed network which adopts the algorithm of Levenberg-Marquarde to train the network. This method uses artificial neural network´s strong nonlinear approximation ability to avoid a lot of nonlinear calculation. At last, in the environment of MATLAB/Simulink, simulation model of the system was built. The simulation results show that the SVPWM algorithm of artificial neural network can improve the switching frequency and reduce the harmonic of output voltage and current.
Keywords :
PWM invertors; feedforward neural nets; power engineering computing; Levenberg-Marquarde algorithm; Matlab; SVPWM algorithm; Simulink; artificial neural network; current harmonic; nonlinear approximation ability; output voltage harmonic; pulse width modulation inverter; space vector PWM inverter; switching frequency; three-layer forward-feed network; three-phase turn-on time; voltage-source inverters; Artificial neural networks; Biological neural networks; Inverters; Space vector pulse width modulation; Training; Vectors; Artificial Neural Network; Inverter; Matlab/Simulink; Space Vector PWM;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2013 Sixth International Symposium on
Conference_Location :
Hangzhou
DOI :
10.1109/ISCID.2013.134