Transformer Condition Prediction Model Based on Gray-TOPSIS and DGA

CHEN Jinqiang; LI Qunzhan

doi:10.13296/j.1001-1609.hva.2015.09.007

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Transformer Condition Prediction Model Based on Gray-TOPSIS and DGA

更新时间：2025-04-26

- Transformer Condition Prediction Model Based on Gray-TOPSIS and DGA
- High VoltageApparatus Vol. 51, Issue 9, Pages: 39-43(2015)
- 作者机构：
  
  西南交通大学电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2015.09.007
  CLC： TM407
- Published：2015
- 稿件说明：
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[1]陈金强,李群湛.基于灰色TOPSIS和DGA的变压器状态预测[J].高压电器,2015,51(09):39-43.

CHEN Jinqiang, LI Qunzhan. Transformer Condition Prediction Model Based on Gray-TOPSIS and DGA[J]. High voltageapparatus, 2015, 51(9): 39-43.
[1]陈金强,李群湛.基于灰色TOPSIS和DGA的变压器状态预测[J].高压电器,2015,51(09):39-43. DOI： 10.13296/j.1001-1609.hva.2015.09.007.

CHEN Jinqiang, LI Qunzhan. Transformer Condition Prediction Model Based on Gray-TOPSIS and DGA[J]. High voltageapparatus, 2015, 51(9): 39-43. DOI： 10.13296/j.1001-1609.hva.2015.09.007.

摘要

以灰色预测和理想点解理论（TOPSIS）为基础

研究基于油中溶解气体体积分数的变压器状态预测及其应用。该方法不同于目前单纯依据数学算法预测油中溶解气体含量的方法

而是从系统的角度综合考虑各特征参数及三比值规则

对故障状态贴近度进行预测。首先根据理想点解法计算各期油中气体体积分数三比值的故障贴近度

以此作为变压器三比值状态信息

然后根据灰色GM（1

1）模型

对变压器三比值故障状态贴进度发展趋势进行预测

最后得到其故障的贴近度

反应了变压器故障状态的发展趋势

对状态维修具有较直观的参考意义。实例数据分析验证了该预测方法的有效性。

Abstract

Based on gray prediction and TOPSIS theory

a condition prediction model of power transformer is proposed. By considering all characteristic parameter and three ratios rule

the model predicts the approaching degree of fault condition instead of the contents of dissolved gases in transformer oil. First

the approaching degree of fault condition is calculated with TOPSIS theory according to the three ratios of the dissolved gases in oil in order to obtain the three ratio condition data of transformer. Then the gray GM（1

1） model is adopted to predict the trend of the approaching degree of three-ratio fault condition

hence to obtain the value of approaching degree

which intuitively reflects development trend of transformer fault condition. Example verifies the validity of the proposed transformer condition prediction model. This study may provide a reference for condition maintenance of transformers.

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