Neural-network-based space-vector pulse-width modulation for capacitor voltage balancing of threephase three-level improved power quality converter

In this study, an artificial neural-network (ANN)-based space-vector pulse-width modulation (SVPWM) for capacitor voltage balancing of a three-phase three-level neutral-point clamped converter with improved power quality is presented. The neural-network-based controller offers the advantage of very fast implementation of the SVPWM algorithm. This makes it possible to use an application specific integrated circuit chip in place of a digital signal processor. The proposed scheme employs single layer feed-forward neural-networks at different stages along with a control algorithm using modified reference vector for capacitor voltage balancing of an improved power quality three-phase neutral-point clamped converter. In other words, the neural-network receives three-phase voltages and currents as input and generates symmetrical pulse-width modulation waves for three phases of the converter. A simulated digital signal processor (DSP)-based modulator generates the data which are used to train the network by a back-propagation algorithm in the MATLAB Neural Network Toolbox. The simulation of converter with ANN-based space-vector modulator shows excellent performance when compared with that of conventional DSP-based modulator.