The development of artificial neural network space vector PWM and diagnostic controller for voltage source inverter

Abstract

This paper presents the development of neural-network-based controller of space vector modulation (ANN-SVPWM) for voltage-source inverters (VSI). This ANN-SVPWM controller completely covers the undermodulation and overmodulation modes with operation extended linearly and smoothly up to square wave (six-step) by using theory of modulation between the limit trajectories. The ANN controller has the advantage of the very fast implementation of an SVM algorithm that can increase the switching frequency of power switches of the static converter. Furthermore, a ANN diagnosis method for real-time fault detection of power switches is proposed in this paper. The ANN controller uses the individual training strategy with the fixed weight and supervised models. The complete ANN-SVPWM and diagnostic controller can be used in power applications such as APF, STATCOM, UPFC and motor drives. A computer simulation program is developed using Matlab/Simulink together with the neural network toolbox for training the ANN-controller