Synthetic Fault Diagnosis for Power Transformers Based on Integration of Evidence Reasoning and Neural Network

QIAN Guochao; ZOU Dexu; YAN Bing; CHEN Weigen; LIU Hongwen; XU Xiaowei; LIU Guangqi

doi:10.13296/j.1001-1609.hva.2015.09.013

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Synthetic Fault Diagnosis for Power Transformers Based on Integration of Evidence Reasoning and Neural Network

更新时间：2025-04-26

- Synthetic Fault Diagnosis for Power Transformers Based on Integration of Evidence Reasoning and Neural Network
- High VoltageApparatus Vol. 51, Issue 9, Pages: 71-76(2015)
- 作者机构：
  
  1. 云南电力试验研究院(集团)有限责任公司电力研究院
  2. 重庆大学输配电装备及系统安全与新技术国家重点实验室
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2015.09.013
  CLC： TM41
- Published：2015
- 稿件说明：
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[1]钱国超,邹德旭,颜冰,陈伟根,刘红文,徐肖伟,刘光祺.集成证据推理与多神经网络的电力变压器故障综合诊断[J].高压电器,2015,51(09):71-76.

QIAN Guochao, ZOU Dexu, YAN Bing, et al. Synthetic Fault Diagnosis for Power Transformers Based on Integration of Evidence Reasoning and Neural Network[J]. High voltageapparatus, 2015, 51(9): 71-76.
[1]钱国超,邹德旭,颜冰,陈伟根,刘红文,徐肖伟,刘光祺.集成证据推理与多神经网络的电力变压器故障综合诊断[J].高压电器,2015,51(09):71-76. DOI： 10.13296/j.1001-1609.hva.2015.09.013.

QIAN Guochao, ZOU Dexu, YAN Bing, et al. Synthetic Fault Diagnosis for Power Transformers Based on Integration of Evidence Reasoning and Neural Network[J]. High voltageapparatus, 2015, 51(9): 71-76. DOI： 10.13296/j.1001-1609.hva.2015.09.013.

摘要

电力变压器是电力系统中最为重要的设备之一

及早发现其潜伏性故障对于保障电网的安全稳定运行具有重要意义。文中结合变压器油色谱和电气预防性试验数据

提出一种基于多神经网络与证据推理的变压器故障综合诊断方法。结合神经网络的输出

将证据融合信度分配中的不可知部分分成由权重分配导致的不可知和信息缺乏导致的不可知两部分组成

优化证据推理算法

较客观地减少了诊断的不确定性。最后通过实例仿真表明该方法具有良好的诊断性能。

Abstract

Earlier detection of latent faults in a power transformer is of great importance to security and stability of power grid. In this study

by making use of the transformer oil chromatography and preventive test data

a synthetic fault diagnosis method for power transformers is proposed based on neural network and evidence reasoning theory. According to the output of neural network

the unknown part of evidence fusion credit distribution is divided into two parts-the unknown part due to weight distribution and the unknown part due to lack of information-for fusion of conflict information

which can objectively reduce the uncertainty of diagnosis and optimize the evidence reasoning algorithm. Simulation proves the superior diagnostic performance of the proposed diagnosis method.

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XIE Qing1

PENG Peng2

TANG Shan3

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Related Institution

International Education Institute, North China Electric Power University

College of Automation,Northwestern Polytechnic University

西北工业大学自动化学院陕西西安710072

Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control,North China Electric Power University,Ministry of Education

Guangdong Shenzhen Electric Power Bureau

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