Research on transformer partial discharge monitoring and improved BP neural network forecasting model

GAO Li-hui; ZHANG Chang-sheng; ZHAO Zhen-gang; HU Wei; CHEN Wu-fen; LI Chuan

doi:10.13873/J.1000-9787(2017)03-0040-03

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Research on transformer partial discharge monitoring and improved BP neural network forecasting model

更新时间：2025-09-11

- Research on transformer partial discharge monitoring and improved BP neural network forecasting model
- Vol. 36, Issue 3, Pages: 40-42(2017)
- 作者机构：
  
  1. 昆明理工大学信息工程与自动化学院
  2. 珠江水利委员会珠江水利科学研究院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2017)03-0040-03
  CLC： TM855;TP183
- Published：2017
- 稿件说明：
移动端阅览
[1]高立慧,张长胜,赵振刚,胡威,陈武奋,李川.变压器局放监测与改进BP神经网络预测模型研究[J].传感器与微系统,2017,36(03):40-42.

GAO Li-hui, ZHANG Chang-sheng, ZHAO Zhen-gang, et al. Research on transformer partial discharge monitoring and improved BP neural network forecasting model[J]. 2017, 36(3): 40-42.
[1]高立慧,张长胜,赵振刚,胡威,陈武奋,李川.变压器局放监测与改进BP神经网络预测模型研究[J].传感器与微系统,2017,36(03):40-42. DOI： 10.13873/J.1000-9787(2017)03-0040-03.

GAO Li-hui, ZHANG Chang-sheng, ZHAO Zhen-gang, et al. Research on transformer partial discharge monitoring and improved BP neural network forecasting model[J]. 2017, 36(3): 40-42. DOI： 10.13873/J.1000-9787(2017)03-0040-03.

摘要

采用传感系统监测变压器局部放电的变化情况

选取高频电流信号和超声波信号作为变压器局部放电的监测参量

利用改进逆传播（BP）神经网络算法对变压器局部放电量进行建模分析。以D9—QY—40000/220型电力变压器（220 k V变压器）为例进行实例研究

结果表明:基于改进BP神经网络的局放预测模型训练集误差系数为0.0118

测试集误差系数为0.0232。此模型的局放预测值与实际值的曲线趋势基本一致

有效地对变压器局部放电量进行预测

为变压器故障诊断奠定了基础。

Abstract

Use sensing system to monitor the changing of the transformer partial discharge

ultrasonic and highfrequency current signal are selected as monitoring parameters of partial discharge and utilize improved backpropagation（ BP） neural network algorithm to make modeling analysis on partial discharge quantity of transformer.With 220 k V Model D9—QTY—40000/220 power transformer as an example for case study. The results show that the error coefficient of test set based on improved BP neural network is 0. 011 8 and the error coefficient of test set is0. 023 2. Prediction value of partial discharge the model is consistent with the actual value

it can effectively predict transformer magnitude of partial discharge and it lay foundation for transformer fault diagnosis.

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references

无线超声传感器网络在开关柜局部放电中的在线监测研究 [J]. 陈武奋,刘爱莲,李英娜,彭庆军,邹立峰,李川. 传感器与微系统 . 2015(03)

基于暂态对地电压和超声阵列信号的变压器局放定位方法 [J]. 程述一,律方成,谢庆,王子建,李燕青,杨海涛. 电工技术学报 . 2012(04)

开关柜局部放电带电检测定位技术的应用与研究 [J]. 孔令明,肖云东,刘娟,曾令甫. 山东电力技术 . 2010(06)

变压器局部放电超高频电磁波的传播特性 [J]. 王国利,单平,袁鹏,郝艳捧,李彦明,郑毅. 高电压技术 . 2002(12)

基于超声波信号的局部放电故障识别算法研究 [D]. 周玲. 华南理工大学 2013

变压器局部放电高频和特高频联合监测法的研究 [D]. 龚细秀. 清华大学 2005

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