Transformer electric field optimization pretreatment method

Abstract

The invention discloses a transformer electric field optimization pretreatment method. The method comprises the steps that 1, adopting an ANSYS parameterized programming command to establish a transformer parameter finite element model; 2, determining the change range of variables to be screened by taking reduction of the maximum electric field intensity as an optimization target; 3, according toa Taguchi algorithm, selecting a change value of each optimization variable, namely an influence factor; 4, establishing an orthogonal table, calling ANSYS through MATLAB to read parameter values in the orthogonal table, and solving an orthogonal matrix; and 5, analyzing a result, eliminating variables with relatively small influence factors, and simplifying a subsequent optimization process. Themethod provided by the invention can be used for screening and processing variables of transformers of any voltage class before electric field optimization.

Description

technical field [0001] The invention relates to the technical field of transformer design, in particular to a pre-processing method for transformer electric field optimization. Background technique [0002] As an important device to maintain the safe and stable operation of the power grid, the electrical insulation of power transformers cannot be ignored. The research, analysis and calculation of the electric field distribution in the transformer is the key to ensure the reasonable and reliable insulation structure of the transformer. [0003] At present, before electric field optimization, there is no process of screening variables. Instead, variables are directly selected to establish models through ANSYS for experiments, and then the optimal values ​​of variables and optimization objectives are determined according to the test results. However, when it is uncertain whether the parameters have a significant impact on the optimization objective, selecting parameters that h...

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Problems solved by technology

However, when it is uncertain whether the parameters have a significant impact on the optimization objective, selecting parameters that have no or less impact on the optimization objective as variables will not only increase the workload, but also affect the accuracy of the optimization results.

In addition, if each test needs to be manually modeled, subdivided, assigned boundary conditions, and extracted the maximum electric field strength in ANSYS, on the one hand, the optimization time is long and the efficiency is low; on the other hand, the accuracy of the test analysis cannot be guaranteed. sex

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