Method for optimizing structure parameter of single-winding magnetic suspension switch reluctance machine

Abstract

The invention discloses a method for optimizing the structure parameter of a single-winding magnetic suspension switch reluctance machine, and belongs to the technical field of magnetic suspension switch reluctance machines. The method comprises the steps that the initial structure parameter of the single-winding magnetic suspension switch reluctance machine is calculated; finite element simulation is conducted on a motor model, the structure parameter to be optimized is selected, and a sample data set is determined; an extreme learning machine algorithm is used for training the sample data set to establish the motor model; the parameter to be optimized is used as an optimized object, and suspension force and torque are used as an optimizing object for conducting parameter optimizing on the motor model. According to the method, an extreme learning machine of a single implicit strata feedforward neural network is used for conducting model identification, iteration is of no need, the motor model can be quickly trained in a high-precision mode, and the parameter design that collaboration of the suspension force and the torque is optimum is achieved by a multi-objective optimizing algorithm.

Description

technical field [0001] The invention discloses a method for optimizing structural parameters of a single-winding magnetic suspension switched reluctance motor, belonging to the technical field of magnetic suspension switched reluctance motors. Background technique [0002] Since the end of the 20th century, the magnetic levitation switched reluctance motor has received extensive attention from researchers. During this period, the magnetic levitation switched reluctance motor with double winding structure is the most researched. The radial force winding and the torque winding are stacked on the same stator pole, so that the radial force winding does not occupy an independent axial space. Because this kind of motor has the advantages of no lubrication, no wear, high power and ultra-high speed operation, it is very suitable for aerospace, high-speed machine tools, flywheel energy storage and other fields. However, the strong coupling between the main winding and the suspension...

AI Technical Summary

Problems solved by technology

[0003] For the structural optimization design of the magnetic levitation switched reluctance motor, the methods that have been proposed include: using the design method combining theoretical analysis and finite element simulation to optimize the structure of the magnetic levitation switched reluctance motor, but because the parameter optimization requires a large number of call calculations model to obtain its output, so the calculation efficiency is low; another method is: use the learning algorithm training of support vector machine to establish a magnetic levitation switched reluctance motor model, and then use the genetic algorithm to optimize the performance of a certain motor for the motor model. Optimizing, this method solves the problem of low computational efficiency of the former, but in the absence of an intelligent algorithm to optimize the parameters of the support vector machine, the accuracy of the motor model established by the support vector machine training is average, and it is difficult to meet the actual needs

In addition, as a single-objective optimization algorithm, the genetic algorithm can only achieve a single maximum of the suspension force or torque, and the separate optimization of the two will inevitably weaken each other.

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