Due to its cheap production costs, simple construction, and robustness, switched reluctance machines are viable candidates for traction motor drive applications in the electrification of transportation. However, high torque ripple and acoustic noise limit the performance of switched reluctance motors (SRM). This thesis considers control methods to reduce the electromagnetic torque ripple in SRM, while also analyzing the impact of these control methods on other aspect of machine performance, such as copper losses and radial force production.
Traditionally, SRM is controlled using rectangular current profiles which are excited using discrete pulsations. Timing of these pulsations is quantified with conduction angles, and the performance of the machine at a given operating point can be optimized by carefully choosing these conduction angles. This thesis starts the analysis on controls of SRM using the conduction angle parameters to determine a baseline of torque ripple performance for comparison against advanced control techniques developed afterwards.
Recently, current profiling techniques have been developed, and have been shown to have high performance for torque ripple reduction. In this thesis, one such technique is proposed in the form of an optimization problem where the solution of this problem yields an optimized current profile that both minimizes torque ripple while reducing copper losses. The proposed current profiling technique ensures good current tracking, which allow for optimal control performance over a wide speed range.
Finally, this thesis shows the torque more generally as one component of the nodal forces in SRM. The other component of the nodal forces is the radial forces, which contributes to the noise, vibrations, and harshness of the machine. In this thesis, modeling of the radial forces has been conducted, and effects of the proposed current profiling technique on radial forces have been shown to comprehensively illustrate the performance of the current profiling technique. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/22326 |
| Date | January 2017 |
| Creators | Li, Haoding |
| Contributors | Emadi, Ali, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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