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 is proposed in this paper that fully covers the linear undermodulation region. A neural network has the advantage of very fast implementation of an SVM algorithm, particularly when a dedicated application-specific IC chip is used instead of a digital signal processor (DSP). 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 pulsewidth modulation waves for the three phases with the help of a single timer and simple logic circuits. The artificial-neural-network (ANN)-based modulator distributes switching states such that neutral-point voltage is balanced in an open-loop manner. The frequency and voltage can be varied from zero to full value in the whole undermodulation range. A simulated DSP-based modulator generates the data which are used to train the network by a backpropagation algorithm in the 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 a 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 the overmodulation region.