Vibration reduction and intelligent control in SRM using optimised two stage commutation
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Date
2025
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Publisher
Inderscience Publishers
Abstract
Switched reluctance motors (SRMs) have grown in popularity in a variety of industrial applications due to their inherent benefits such as high fault-tolerance, simplicity, affordability, and rare-earth free nature. However, the generation of undesirable vibrations due to radial force variations remains a significant challenge. Two stage commutation based on active vibration cancellation (AVC) is an effective method to reduce these vibrations. The focus of this paper is to address the major limitation with two stage commutation, namely the extended tail current causing increased copper loss. This is accomplished with optimal commutation parameters employing particle swarm optimisation (PSO) method. A MATLAB/Simulink model of SRM with vibration signal is developed and is used for demonstrating vibration cancellation. An intelligent control is also implemented which can track the dynamic changes in speed-load conditions. This paper showcases that this approach is an effective solution to reduce the vibrations issues in SRM, thereby improving the overall performance of the motor for industrial applications. © © 2025 Inderscience Enterprises Ltd.
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Keywords
Electric commutation, Fault tolerance, Intelligent control, MATLAB, Reluctance motors, Ventilation exhausts, Active vibration cancellation, ANN, Neural-networks, Particle swarm, Particle swarm optimization, Swarm optimization, Switched Reluctance Motor - SRM, Two stage commutation, Neural networks, Particle swarm optimization (PSO)
Citation
International Journal of Power Electronics, 2025, 21, 5, pp. 471-496
