A neural network based space vector PWM controller for a three-level voltage-fed inverter induction motor drive

A neural network based implementation of space vector modulation (SVM) of a three-level voltage-fed inverter has been proposed in this paper that fully covers the linear undermodulation region. The neural network has the advantage of very fast implementation of SVM algorithm, particularly when a dedicated ASIC chip is used instead of a digital signal processor. A three-level inverter has a large number of switching states compared to a two-level inverter and therefore the SVM algorithm to be implemented in a neural network is considerably more complex. In the proposed scheme, a three-layer feedforward neural network receives the command voltage and angle information at the input and generates symmetrical PWM waves for the three phases with the help of a single timer and simple logic circuits. The ANN based modulator distributes switching states such that neutral point voltage is balanced in open loop manner. The frequency and voltage can be varied from zero to full value in the whole undermodulation range. A DSP based modulator generates the data which are used to train the network by backpropagation algorithm in MATLAB/Neural Network Toolbox. The performance of an open loop volts/Hz speed controlled induction motor drive has been evaluated with the ANN based modulator and compared with that of conventional DSP based modulator, and shows excellent performance. The modulator can be easily applied to a vector-controlled drive, and its performance can be extended to overmodulation region.