Abstract
Direct torque control (DTC) of Switched reluctance motor is known straightforward control structure with similar execution to that of field situated control strategies. In any case, the part of ideal determination of the voltage space vector is one of the weakest focuses in a routine DTC drive because of adjustable switching frequency and high torque ripple. In this paper, ideal choice of voltage space vectors is accomplished utilizing ANFIS (Adaptive Neuro Fuzzy Inference System) with space vector Modulation. SVM-DTC gives consistent switching frequency and the proposed ANFIS controller’s structure manages the torque and stator flux error signals through the fuzzy deduction to get a yield that takes the type of space voltage vector. Simulation results accept the proposed evolutionary system with quick torque and flux reaction with minimized torque ripple and flux ripple.
Highlights
Switched reluctance motor, the doubly salient, separately energized motor has basic and simple construction
Space vector modulation (SVM) modulator is joined with direct torque control for prompting engine drives as appeared in [9]-[11] to give a consistent inverter switching frequency
The control algorithm executed is the hybrid of fuzzy control and neural system with the SVM-Direct Torque Control (DTC) for SRM drive
Summary
The doubly salient, separately energized motor has basic and simple construction. The simple converter topology and dynamic algorithm because of the unipolar operation staying away from shoot through deficiencies makes SRM invaluable in utilizations of aviation, which require high unwavering quality It finds wide application in commercial car enterprises, direct drive machine apparatuses and so forth [1]. Space vector modulation (SVM) modulator is joined with direct torque control for prompting engine drives as appeared in [9]-[11] to give a consistent inverter switching frequency. Noticing that the torque ripple and acoustic noise issue for this SVM-based DTC methodology are essentially enhanced for invoking zero inverter switching state inside each switching time of inverter control. The control algorithm executed is the hybrid of fuzzy control and neural system with the SVM-DTC for SRM drive. At each sampling time the voltage vector selection picks the inverter switching state which diminishes the instantaneous flux and torque errors
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