Life Prediction of Oil Paper Insulation of Transformer Based on GRNN Neural Network

LIN Zhe; LAN Sheng; ZHANG Yuhang

doi:10.13296/j.1001-1609.hva.2015.02.021

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Life Prediction of Oil Paper Insulation of Transformer Based on GRNN Neural Network

更新时间：2025-04-26

- Life Prediction of Oil Paper Insulation of Transformer Based on GRNN Neural Network
- High VoltageApparatus Vol. 51, Issue 2, Pages: 125-130(2015)
- 作者机构：
  
  福州大学电气工程与自动化学院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2015.02.021
  CLC： TP18;TM406
- Published：2015
- 稿件说明：
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[1]林喆,兰生,张宇航.基于广义回归神经网络的油纸绝缘变压器的寿命预测[J].高压电器,2015,51(02):125-130.

LIN Zhe, LAN Sheng, ZHANG Yuhang. Life Prediction of Oil Paper Insulation of Transformer Based on GRNN Neural Network[J]. High voltageapparatus, 2015, 51(2): 125-130.
[1]林喆,兰生,张宇航.基于广义回归神经网络的油纸绝缘变压器的寿命预测[J].高压电器,2015,51(02):125-130. DOI： 10.13296/j.1001-1609.hva.2015.02.021.

LIN Zhe, LAN Sheng, ZHANG Yuhang. Life Prediction of Oil Paper Insulation of Transformer Based on GRNN Neural Network[J]. High voltageapparatus, 2015, 51(2): 125-130. DOI： 10.13296/j.1001-1609.hva.2015.02.021.

摘要

文中基于广义回归神经网络（GRNN）技术挖掘数据变化规律

构建了GRNN神经网络预测模型

运用该模型对油纸绝缘变压器进行寿命预测。将变压器绝缘纸老化过程中生成的特征产物如糠醛、CO2和CO的质量分数

以及相关时间参量作为模型输入。将所采集的多组油纸绝缘变压器的测试样本作为基础数据

运用该模型对相应的变压器进行寿命预测。结果表明

模型寿命预测的输出值与实际值基本一致

从而验证了模型的合理性

这对监测绝缘材料老化状态的进一步研究具有现实意义。

Abstract

This paper is based on the generalized regression neural network（GRNN）theory

finding the rules of data by it

constructing prediction model of GRNN

then use it to forecast the life of oil paper insulation transformers. In the process of aging

insulating paper of a transformer will generate feature products such as furfural

CO2 and CO. The content of each product and the corresponding time parameter is used as the model input.The test samples are collected as the basic data

to predict the life of corresponding transformers by using this model. Comparison of the results of GRNN and the measured data show that the results of GRNN are in accordance with the measured values

which proves the rationality of the model. And it is also significant for the further study of material aging status of insulation monitoring.

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