The Global Optimization Of Torque Ripple For SR Motor Based On The Particle Swarm Algorithm

The instantaneous torque and vibration, noise caused by ripple of Switched Reluctance(SR) motor are not only more prominent problem, but also the difficulties in SRD research. Atpresent how to suppress the torque ripple has become a hot spot, but the study focuses more onthe control optimization and the motor body optimization relatively few, furthermore most ofthe existing control have not considered the torque from mutual inductance. To solve the aboveproblem, based on the analysis of the torque ripple mechanism, and combined with the fastconvergence and global optimization characteristics of the particle swarm algorithm, this paperultimately achieve torque ripple global optimization of the SR motor mainly through improveddesign of the motor body, power topology optimization, and instantaneous torque compensationcontrol.First, the complete structure design of the motor is achieved by the systematic derivationof the SR motor output equation. For the special four-phase8/6pole SR motor, this paper usesthe steady-state finite element method to analyze the differences beween single and two-phaseexcitation mode as well as long and short magnetic circuit in characteristics of flux, inductanceand torque, taking the mutual inductance coupling, saturation effects into account. Theself-inductance and mutual inductance characteristics of SR motor were measured to verify theaccuracy of the finite element simulation. The SR magnetic field-electric circuit-movementcoupling analysis model was established, while time stepping finite element method wasintroduced to study characteristics of the SR motor from start-up to steady-state operating indetail.Second, from the perspective of the motor inherent performance considerations, this paperstudied how to optimize motor performance by design process. The effect of motor pole shapeon the objective functions associated with the optimization variables and constraints wereobtained by finite element analysis. Through the training of generalized regression neuralnetwork, the mathematical models of the objective function were obtained, and adaptiveparticle swarm algorithm was used to solve the optimal design of the SR motor with nonlinear,multi-variable, multi-constraint conditions. The calculation program can be extended to anynumber of phases, and any structure motor, so it can provide a general algorithm for SR motoroptimal design.At the same time, from the view of both motor control and power topology, the doublephases conduction equations including the mutual inductance were derived, and nonlinearsimulation model of the SR motor was carried out using Simulink. The effects of switchingangles and chopping frequency on torque ripple were studied in-depth, so provide a strong basisto choose a reasonable control parameters; the new power topology based on capacitivecompensation was proposed, which improves system power factor and efficiency whilereducing torque ripple.Finally, this paper presents the instantaneous torque control strategy based on generalizedregression neural network with particle swarm optimization, and for the double phasesexcitation mode combined with the torque compensation principle to jointly achieve the the torque ripple optimal control strategy, the method, combined with the torque inverse model, cancompensate the mutual inductance torque due to asymmetric electromagnetic, and torque rippleis significantly reduced.The SRD system experimental platform is built to complete the steady–state experiment,dynamic performance experiment, and the application of the instantaneous torque control andordinary PWM control are used, then the effects of different control modes on phase current,output torque and torque ripple is analyzed. The experimental results show that the stable phasecurrent, robustness, and better anti-interference ability, at all a global optimization control of theSR motor torque ripple is achieved.