This thesis presents advanced torque control methods for torque ripple reduction and performance improvement in switched reluctance motor (SRM) drives.
A new offline torque sharing function (TSF) method is proposed for torque ripple reduction in SRMs. The proposed TSF achieves lower current tracking error by establishing a new current reference generation strategy. The phase current reference is first derived from the torque command using offline calculations and also from the phase current response that is obtained from the dynamic model of the SRM. Then, an optimization problem is formulated to shape the current reference for the objective of minimizing the torque ripple and copper losses, while maintaining the required average output torque at the given operating speed. The dynamic simulation of the SRM model is also utilized in the optimization problem.
A new online TSF method is proposed for torque ripple reduction in SRMs. The proposed TSF takes the current dynamics and induced electromotive force into account by establishing a new online current profile generation technique. First, a primary phase current reference derived from the torque reference is applied to the SRM. Then, the decaying phase current after the turn-off angle is sampled, and it is used to update the current reference. A new online optimization strategy is performed to shape the current reference during the operation of the machine. Owing to the proposed current profile generation technique, the optimization process is decoupled to independently minimize the torque ripple by optimizing the turn-on angle and minimizing copper losses by optimizing the turn-off angle.
Compared to the conventional TSFs and existing optimization-based TSFs, the proposed two TSFs achieve accurate torque control, improved torque-speed capability, reduced torque ripple, and better current tracking performance. All the proposed TSF methods are validated by both simulations and experiments on a 3-phase, 12/8 SRM. / Dissertation / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/26094 |
| Date | January 2020 |
| Creators | Xia, Zekun |
| Contributors | Emadi, Ali, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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