Multi-factor efficient optimization design method for motor bar surface electric field

A surface electric field and optimization design technology, applied in neural learning methods, design optimization/simulation, genetic models, etc., can solve the problems of high risk, high test cost, low efficiency, etc., to improve the accuracy of the model and overcome the high cost. Effect

Pending Publication Date: 2021-02-09
HARBIN UNIV OF SCI & TECH +1
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Problems solved by technology

[0005] The purpose of the present invention is to propose a multi-factor optimization design method for the electric field of the motor wire rod surface, which is used to obtain the

Method used

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  • Multi-factor efficient optimization design method for motor bar surface electric field
  • Multi-factor efficient optimization design method for motor bar surface electric field
  • Multi-factor efficient optimization design method for motor bar surface electric field

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Embodiment Construction

[0023] Below in conjunction with example the present invention will be further described, but not as limiting the present invention.

[0024] figure 1 The flow chart of the multi-factor optimization design method for the surface electric field of the motor bar is shown. The following is an example of the optimization of the end structure of the stator bar of the hydro-generator with a rated voltage of 24kV and a rated capacity of 1000MW:

[0025] Step S1, select the optimization target and obtain the initial training data set:

[0026] The optimization goal in this example is to adjust the length of medium resistance, length of medium and high resistance, resistivity of medium resistance, resistivity of medium and high resistance, and resistivity of high resistance of anti-corona layer of motor wire rod so that the maximum tangential electric field on the surface of motor wire rod is lower than 1.5kV / cm. Combined with the finite element simulation model of the end of the el...

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Abstract

The invention relates to the field of large generator bar performance simulation analysis, in particular to a multi-factor optimization design method for a motor bar surface electric field. Accordingto the method, a neural network and a genetic algorithm are combined, and design key points of the motor bar can be optimized in a targeted manner according to performance requirements, for example, the conductivity and the length of each section of material of the anti-corona layer of the bar are optimized according to requirements of the bar on electric field homogenization capacity. According to the method, the problems that in the motor bar design process, the motor bar performance influence factors are excessive, and the optimal design scheme is not easy to obtain are solved, the multi-factor optimization capacity is achieved, and the capacity of improving the motor bar design quality is achieved.

Description

technical field [0001] The invention relates to the field of performance simulation analysis of large-scale generator wire rods, in particular to a multi-factor optimization design method for the surface electric field of motor wire rods. Background technique [0002] With the continuous development of my country's industry, the society's demand for electricity has increased significantly, which has directly led to an increase in the demand for large generators. The running stability of the generator is greatly affected by the insulation, especially at the end of the stator bar of the generator, there will be obvious electric field concentration phenomenon, which is mainly manifested as corona discharge. In order to make the electric field distribution on the surface of the motor bar uniform, it is necessary to consider various influencing factors in the design, such as the length of each section of the anti-corona layer, the resistivity of each section of the anti-corona la...

Claims

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Application Information

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IPC IPC(8): G06F30/17G06F30/27G06F30/23G06N3/04G06N3/08G06N3/12
CPCG06F30/17G06F30/27G06F30/23G06N3/084G06N3/123G06N3/126G06N3/045
Inventor 郭宁刘蓓蕾何明鹏高俊国张跃张晓虹谢志辉胡波梁智明
Owner HARBIN UNIV OF SCI & TECH
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