AbstractTorque ripple exists at the output of permanent magnet synchronous motors (PMSMs) as a result of current measurement error, non-sinusoidal back-electromotive force (EMF) and cogging torque. Vibrations caused by torque ripple are transmitted through the mechanical system and then interact with the motor housing and cause acoustic emissions. These acoustic emissions limit the applications available to PMSMs. Since part of the cogging torque component is based on manufacturing error, motor design alone cannot eliminate all of the torque ripple. However, torque ripple and hence acoustic emissions can be reduced via control of the output torque. If an estimate of the torque ripple can be determined, and its inverse then added to the torque reference used as the input to the motor controller, the motor will produce a torque to counter the torque ripple generated.
To determine an estimate of the torque ripple and then minimise the acoustic emissions, this thesis proposes a control method that uses a microphone to sample the acoustic emissions and then determines the relationship between the measured emissions and the torque ripple, for a number of orders (position dependant frequencies) simultaneously. Experimental results show that there is good coherence between torque ripple and acoustic emissions at the orders associated with torque ripple. The method was tested using both a high quality microphone and a low cost electret microphone.
The motor used in this research was a surface magnet or non-salient machine. The proposed compensation method would work equally well on a salient PMSM as the compensation signal is applied to the reference torque signal for the motor controller and is independent of the motors saliency.
The proposed method was shown to be effective in significantly reducing the acoustic emissions caused by torque ripple, using both the high quality microphone and the electret microphone. After reduction, the magnitude of the acoustic emissions was similar to that of the background noise at other frequencies. This represents a reduction of between 68% to over 99% of the original signal.
|Date of Award||2016|
|Supervisor||Friso De Boer (Supervisor), Greg Heins (Supervisor) & Jai Singh (Supervisor)|