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Addressing drawbacks inherent to switched reluctance machines (SRM) could allow its distinctive characteristics to broaden applications from selective, niche industrial roles to various engineering sectors. Phase winding isolation is a contributing factor of advantageous characteristics unique to SRMs when compared to the industry workhorse, pulse width modulation driven induction machines. Specifically, this increases fault tolerance, simplifies the manufacturing winding process, and allows the machine to remain in a locked rotor position safely without concern of faulting, thus contributing to a greater overall robustness. On the contrary SRMs, when compared to other electrical machine types, are most notably criticized for requiring complex control strategies to achieve optimal operation, having greater overall current requirements, operating at higher speeds, and creating greater acoustic noise and torque ripple. Cumulatively, these shortcomings alienate the SRMs commercial and industrial popularity, ultimately limiting its full potential from being exploited. Since SRM torque production is typically non-linear, various techniques have been developed in order to produce the maximum torque per given current excitation, i.e. maximum torque per ampere (MTA). The “balanced commutator” control strategy uses a look-up table to account for the non-linearity of the SRMs torque-angle characteristic, yet does not totally optimize the copper or iron losses, current requirements, or effectively mitigate the torque ripple. A stochastic search technique based on evolutionary algorithms, particle swarm optimization (PSO), allows for MTA profiles to be obtained that optimize the shortcomings inherent to the balanced commutator technique. This work presents a novel MTA SRM control strategy based on the PSO technique. The optimum phases current profiles of a 4-phase, 8/6 pole SRM are obtained such that copper losses and torque ripple are minimized while achieving the desired torque at specific rotor positions. Results are compared against the balanced commutator method. [ABSTRACT FROM PUBLISHER]

While currently occupying only a niche role in industrial applications, the switched reluctance machines (SRM) unique operational characteristics could prove useful for additional engineering sectors given that inherent drawbacks are addressed. Phase winding isolation of SRMs provides greater fault tolerance when compared to the industrial standard, pulse width modulation driven induction machines. Furthermore, they may remain in a locked rotor position safely without concern of faulting and have higher speeds than many other electrical machines, i.e. contributing to greater overall robustness. When compared to other electrical machines, the SRM has higher currents requirements, creates greater acoustic noise and torque ripple, and requires more advanced controls for effective operation. Such drawbacks alienate the SRMs commercial and industrial popularity, ultimately limiting its full potential from being exploited. Since SRM torque production is typically non-linear, various techniques have been developed in order to maximize the torque output per unit current excitation, i.e. maximum torque per ampere (MTA). The “conventional” strategy, while simplistic, assumes a constant excitation over a symmetric period of the machine. This increases copper and iron losses while not effectively mitigating the current requirements or inherent torque ripple. By using particle swarm optimization (PSO), a stochastic search technique based on evolutionary algorithms, phase current MTA profiles may be obtained that optimize such conditions. This work presents a novel MTA SRM control strategy based on the PSO technique that obtains the optimum phase current profiles of a 4-phase, 8/6 pole SRM such that copper losses and torque ripple are minimized while achieving the desired torque at specific rotor positions. [ABSTRACT FROM PUBLISHER]