Experimental Evaluation of the Impact of Conduction Angle Optimization on the Acoustic Noise Levels of a Switched Reluctance Motor

Switched Reluctance Motors (SRMs) are increasingly attractive for applications requiring robust performance and cost efficiency. However, their widespread adoption can be limited by high torque ripple, which induces additional vibrations and acoustic noise. This study examines the impact of optimizing conduction angles on the acoustic noise levels of a 12/8 SRM. A multi-objective Genetic Algorithm (GA), paired with a model-independent hysteresis current controller, is employed to simultaneously enhance average torque and minimize torque ripple. Due to the unavailability of detailed motor geometries to obtain the motor's static characteristics, experimentally determined electromagnetic characteristics are used for optimization. The motor's acoustic performance is assessed by calculating its sound power level from sound intensity measurements, ensuring a reliable evaluation independent of probe distance and positioning. These measurements are obtained at 10 different operating points using 12 probes arranged in a semi-circular configuration. Experimental results indicate that optimizing conduction angles effectively reduce torque ripple and acoustic noise, thereby enhancing overall motor performance.