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

A neural network based implementation of space vector modulation of a voltage-fed inverter has been proposed in this paper that fully covers the undermodulation and overmodulation regions linearly extending operation smoothly up to square wave. The neural network has the advantage of very fast implementation of SVM algorithm that can increase the converter switching frequency, particularly when a dedicated ASIC chip is used in the modulator. Two ANN-based SVM techniques have been validated: an indirect method with the help of a timer that generates the PWM waveforms from the command voltage vector at the input, and a direct method that synthesizes waveforms directly without any timer. The indirect method has been fully implemented and extensively evaluated in a volts/Hz controlled 5 hp, 60 Hz, 230 V induction motor drive. The performances of the drive with ANN-based SVM are excellent. The scheme can be easily extended to vector-controlled drive. The direct method, although has a simpler topology, needs very large training data and training time