2019 IEEE International Electric Machines &Amp; Drives Conference (IEMDC) 2019
DOI: 10.1109/iemdc.2019.8785171
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Analytical and Experimental Verification of Novel Current Waveforms for Noise Reduction in Switched Reluctance Motor

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Cited by 14 publications
(6 citation statements)
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“…With proper numerical optimization, this reference current can achieve low torque ripple, low sum of the radial force, and similar efficiency when compared with the conventional square reference current. More experimental verification of reference current (19) was recently reported in [185] and [186].…”
Section: B Current Profilingmentioning
confidence: 80%
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“…With proper numerical optimization, this reference current can achieve low torque ripple, low sum of the radial force, and similar efficiency when compared with the conventional square reference current. More experimental verification of reference current (19) was recently reported in [185] and [186].…”
Section: B Current Profilingmentioning
confidence: 80%
“…Yes No [121], [122], [178]- [185], [187]- [190] DIFC Effective in a wide speed range, torque ripple can also be considered…”
Section: Discussionmentioning
confidence: 99%
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“…An optimization algorithm is employed to eliminate the higher temporal order of radial force density by using Gaussian-shaped radial force reference [7]. Also, flattening the summation of radial forces has been studied in [28]. Direct Instantaneous Force Control (DIFC) has been employed to reduce the mode-0 related vibration by controlling the overall radial force [29].…”
Section: B Noise Control At the Sourcementioning
confidence: 99%
“…Huang H B et al [9] proposed a new noise source identification method based on interval analysis to identify and reduce the noise generated by the suspension structure of electric vehicles. Furqani J et al [10] analyzed and tested a certain type of switched reluctance motor and verified that adopting new current waveform could reduce noise. Literature [11][12][13] has modeled and suppressed motor noise, providing theoretical basis and analytical method for noise prediction, optimization and evaluation of motors.…”
Section: Introductionmentioning
confidence: 99%