TFSRM multi-objective optimization method based on improved genetic algorithm

Abstract

The invention discloses a TFSRM multi-objective optimization method based on an improved genetic algorithm, which comprises the following steps: establishing a TFSRM optimization design mathematical model; the optimization problem of objective function being transformed into a fixed point problem; the solution space of the optimization problem being simply partitioned by J3; the parameter set of optimization problem being coded; evaluation and selection of fitness functions; the vertices of the bearing simplex being integer-labeled to calculate individual fitness; repeated selection, crossover, mutation and dimensionality increment being performed until all the bearing simplexes in the population evolve into full standard simplexes, and the operation being stopped to obtain an approximateglobal optimal solution. The invention organically combines the traditional genetic algorithm with the simple homotopy algorithm in the fixed point theory and is applied to the TFSRM structural parameter optimization. The improved genetic algorithm improves the global precision and the convergence speed of the traditional genetic algorithm, and the method is very suitable for the multi-objective optimization of the TFSRM.

Description

technical field [0001] The invention relates to the technical field of transverse flux motors, in particular to a multi-objective optimization method for a transverse flux switched reluctance motor (TFSRM) based on an improved genetic algorithm. Background technique [0002] The transverse flux switched reluctance motor is a high power drive motor. It has many advantages in the field of motors, such as diverse topological structures, strong structural design flexibility, good fault tolerance, and high power density. Due to the above advantages, transverse flux switched reluctance motors have received extensive attention in recent years, especially in direct drive fields such as electric vehicles, ship direct drives, wind power, and electric servo systems. But at the same time, the transverse flux switched reluctance motor itself has some problems, such as low operating efficiency, large output torque ripple, difficult optimization design, etc. These problems limit the indus...

AI Technical Summary

Problems solved by technology

But at the same time, the transverse flux switched reluctance motor itself has some problems, such as low operating efficiency, large output torque ripple, difficult optimization design, etc. These problems limit the industrial application of transverse flux switched reluctance motors. application

[0003] For the structural optimization design of transverse flux switched reluctance motors, the methods that have been proposed include: adopting the design method combining theoretical analysis and finite element simulation to optimize the structure design of transverse flux switched reluctance motors, but because parameter optimization requires a large number of call The computer model obtains its output, so the calculation efficiency is low; another method is to establish a transverse flux switched reluctance motor model, and then use the genetic algorithm to optimize the motor model with some motor performance as the optimization target. However, traditional genetic algorithms generally have shortcomings such as slow optimization speed, high cost, and low efficiency, which prolong the cycle of motor design

Images