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Motor parameter design method and system based on grey wolf algorithm

A technology of motor parameters and design methods, applied in design optimization/simulation, multi-objective optimization, etc., to shorten the number of iterations, avoid nonlinear errors, and improve the efficiency of optimization

Inactive Publication Date: 2022-07-15
佛山仙湖实验室
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Problems solved by technology

[0004] The present invention provides a motor parameter design method and system based on Gray Wolf Algorithm to solve one or more technical problems existing in the prior art, and at least provide a beneficial choice or create conditions

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  • Motor parameter design method and system based on grey wolf algorithm
  • Motor parameter design method and system based on grey wolf algorithm
  • Motor parameter design method and system based on grey wolf algorithm

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Embodiment Construction

[0040] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0041] It should be noted that although the functional modules are divided in the schematic diagram of the system and the logical sequence is shown in the flowchart, in some cases, the modules can be divided differently from the system, or executed in the order in the flowchart. steps shown or described. The terms "first", "second", etc. in the description and claims and the above drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0042] Please refer to figure 1 , figure 1 It is a schematic flowchart of a motor param...

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Abstract

The invention discloses a motor parameter design method and system based on a grey wolf algorithm, which are mainly applied to the technical field of motor optimization, and the method comprises the following steps: step 100, constructing a motor finite element model, and determining a to-be-optimized parameter set of the motor finite element model and an associated optimization objective function set; step 200, based on the motor finite element model, performing automatic optimization on the to-be-optimized parameter set by using a grey wolf algorithm to obtain an optimal Pareto solution set library; and step 300, obtaining the to-be-optimized parameter set information which enables the optimization objective function set information to reach the minimum value from the optimal Pareto solution set library as the optimal parameter of the motor. According to the method, the motor finite element model, the grey wolf algorithm and the Pareto dominating relation are introduced for iterative calculation, the optimization efficiency of the optimal parameters of the motor can be effectively improved, and the number of iterations is reduced.

Description

technical field [0001] The invention relates to the technical field of motor optimization, in particular to a method and system for designing motor parameters based on a gray wolf algorithm. Background technique [0002] Existing commercial motor optimization methods can be roughly divided into direct optimization methods and indirect optimization methods, among which: direct optimization methods are usually based on the equivalent magnetic circuit model, analytical model or finite element model of the motor, using single or multi-objective genetic algorithms, particle The swarm algorithm directly performs single or multi-parameter optimization; the indirect optimization method obtains the sensitivity of the optimization objective to each parameter by analyzing the influence of different parameter combinations to be optimized on the optimization objective, and then obtains the equivalent optimization objective-parameter function to be optimized. , for fast indirect optimizat...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06F30/23G06F111/06
CPCG06F30/27G06F30/23G06F2111/06
Inventor 解文龙肖从达
Owner 佛山仙湖实验室
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