Analysis and Prediction of Transformer Overheating Fault Based on Chaotic Characteristics of Oil Chromatogram

ZHANG Pengfei; ZHOU Lixing; ZHANG Zhuodong; LI Jia

doi:10.13296/j.1001-1609.hva.2019.08.034

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Analysis and Prediction of Transformer Overheating Fault Based on Chaotic Characteristics of Oil Chromatogram

更新时间：2025-04-26

- Analysis and Prediction of Transformer Overheating Fault Based on Chaotic Characteristics of Oil Chromatogram
- High VoltageApparatus Vol. 55, Issue 8, Pages: 237-243(2019)
- 作者机构：
  
  长沙理工大学电气与信息工程学院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2019.08.034
  CLC： TM407
- Published：2019
- 稿件说明：
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[1]张鹏飞,周力行,张倬栋,黎佳.变压器油色谱混沌特性的过热故障分析及预测[J].高压电器,2019,55(08):237-243.

ZHANG Pengfei, ZHOU Lixing, ZHANG Zhuodong, et al. Analysis and Prediction of Transformer Overheating Fault Based on Chaotic Characteristics of Oil Chromatogram[J]. High voltageapparatus, 2019, 55(8): 237-243.
[1]张鹏飞,周力行,张倬栋,黎佳.变压器油色谱混沌特性的过热故障分析及预测[J].高压电器,2019,55(08):237-243. DOI： 10.13296/j.1001-1609.hva.2019.08.034.

ZHANG Pengfei, ZHOU Lixing, ZHANG Zhuodong, et al. Analysis and Prediction of Transformer Overheating Fault Based on Chaotic Characteristics of Oil Chromatogram[J]. High voltageapparatus, 2019, 55(8): 237-243. DOI： 10.13296/j.1001-1609.hva.2019.08.034.

摘要

在利用油色谱数据对电力变压器进行故障诊断时

为了改善因油色谱数据的波动及其非线性所带来的干扰

文中在利用油色谱分析过热故障时

引入了混沌理论对非线性系统进行分析。文中通过相空间重构建立油色谱数据混沌时间序列

并进行系统混沌特性判定

再将油色谱数据的波动以混沌特征量进行表征

比较分析过热故障特征气体在不同监测数据段中的混沌特征参量变化

从而提取油色谱非线性数据中所包含的状态信息。然后

基于混沌序列具有的短期预测性

利用最大Lyapunov指数预测法对油中气体体积分数的趋势进行预测。实例分析结果表明:过热故障特征气体的混沌特性参量能准确地进行过热故障的识别;在分析相应混沌时间序列后

可以准确预测油中溶解气体体积分数及其短期变化趋势。实例验证了该方法在变压器过热故障识别时的有效性。

Abstract

For the purpose of reducing the interference due to fluctuation and nonlinearity of oil chromatographic data in the fault diagnosis of transformer using oil chromatographic data

the chaos theory was introduced to analyze the nonlinear system.First

the chaotic time series of oil chromatographic data were established by phase space reconstruction

the chaotic characteristics of the system were determined

and the fluctuation of oil chromatographic data was represented by the chaotic characteristic parameters.The variations of the chaotic characteristic parameters in different monitoring data segments of the characteristic gas from overheaing fault were compared

and the state information in the nonlinear data of the oil chromatogram was extracted.Based on the short-term predictability of chaotic series

the variation trend of gas content in oil was predicted by the largest Lyapunov exponent prediction method.Case analysis shows that the chaotic characteristic parameters of characteristic gases from overheating fault could precisely identify the overheating fault

and the content of dissolved gas in oil and its short-term variation trend could be accurately predicted after analyzing the corresponding chaotic time series.The example verifies the effectiveness of this method.

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