Inderscience Publishers

Neuro–fuzzy–based space vector modulation for THD reduction in VSI fed induction motor drive

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Space vector modulation is an optimal pulse width modulation technique for variable speed drive application. This paper proposes adaptive neuro–fuzzy inference system (ANFIS) based space vector modulation (SVM) technique for voltage source inverter. The proposed ANFIS network is independent of the switching frequency and uses hybrid learning algorithm for training. Due to this learning algorithm, SVM algorithm can be implemented very fast and the desired training error can be obtained with less number of iterations compared to other optimisation techniques like neural, fuzzy and genetic. The performance of ANFIS controlled drive is compared with the conventional SVM–based drive. The simulation results of inverter phase voltages obtained are verified experimentally using a Dspace kit (DS1104). The % THD value of simulation and experimental waveforms of inverter phase voltages for 3 kHz switching frequency is presented.

Keywords: adaptive neuro–fuzzy inference system, ANFIS, space vector modulation, SVM, induction motors, voltage source inverters, neural networks, fuzzy logic, pulse width modulation, hybrid learning, THD reduction, total harmonic distortion

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