A Prediction of the Oil-filled Transformer Based on BP Neural Networks

LIN Zhao-hui; ZHANG Bi-de

doi:10.13296/j.1001-1609.hva.2010.04.008

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A Prediction of the Oil-filled Transformer Based on BP Neural Networks

更新时间：2025-04-26

- A Prediction of the Oil-filled Transformer Based on BP Neural Networks
- High VoltageApparatus Vol. 46, Issue 4, Pages: 84-87(2010)
- 作者机构：
  
  西华大学电气信息学院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2010.04.008
  CLC： TM411
- Published：2010
- 稿件说明：
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[1]林朝晖,张彼德.基于BP神经网络的油浸式变压器寿命预测[J].高压电器,2010,46(04):84-87.

LIN Zhao-hui, ZHANG Bi-de. A Prediction of the Oil-filled Transformer Based on BP Neural Networks[J]. High voltageapparatus, 2010, 46(4): 84-87.
[1]林朝晖,张彼德.基于BP神经网络的油浸式变压器寿命预测[J].高压电器,2010,46(04):84-87. DOI： 10.13296/j.1001-1609.hva.2010.04.008.

LIN Zhao-hui, ZHANG Bi-de. A Prediction of the Oil-filled Transformer Based on BP Neural Networks[J]. High voltageapparatus, 2010, 46(4): 84-87. DOI： 10.13296/j.1001-1609.hva.2010.04.008.

摘要

为了保证变压器的安全运行从而提高其经济效益

对变压器进行剩余寿命预测并采取相应对策显得尤为重要。研究表明

通过对油浸式电力变压器油中溶解的糠醛体积分数以及气体测定可以进行变压器固体绝缘寿命的监督。为此

笔者利用BP神经网络建立起油中CO、CO2、糠醛含量以及运行年限来预测绝缘纸老化程度的关系模型。实例结果表明

通过此方法能够提供比较准确的变压器寿命预测。

Abstract

The oil-filled transformers’ solid insulating life can be measured by detecting the content of furfural and the gases both dissolved in oil.According to this principle

the relational model

with the help of BP neural networks

is to predict the aging degree of insulating paper by operated length and the content of furfural dissolved in oil.The result of examples show that this method can predicts exactly the life of transformer.

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