Dissolved Gas Analysis Based QPSO-BP Model for Transformer Fault Diagnosis

CHENG Jiatang; DUAN Zhimei; XIONG Yan; AI Li

doi:10.13296/j.1001-1609.hva.2016.02.009

您当前的位置：

首页 >

文章列表页 >

Dissolved Gas Analysis Based QPSO-BP Model for Transformer Fault Diagnosis

更新时间：2025-04-26

- Dissolved Gas Analysis Based QPSO-BP Model for Transformer Fault Diagnosis
- High VoltageApparatus Vol. 52, Issue 2, Pages: 57-61(2016)
- 作者机构：
  
  红河学院工学院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2016.02.009
  CLC： TM407
- Published：2016
- 稿件说明：
移动端阅览
[1]程加堂,段志梅,熊燕,艾莉.基于DGA的QPSO-BP模型变压器故障诊断方法研究[J].高压电器,2016,52(02):57-61.

CHENG Jiatang, DUAN Zhimei, XIONG Yan, et al. Dissolved Gas Analysis Based QPSO-BP Model for Transformer Fault Diagnosis[J]. High voltageapparatus, 2016, 52(2): 57-61.
[1]程加堂,段志梅,熊燕,艾莉.基于DGA的QPSO-BP模型变压器故障诊断方法研究[J].高压电器,2016,52(02):57-61. DOI： 10.13296/j.1001-1609.hva.2016.02.009.

CHENG Jiatang, DUAN Zhimei, XIONG Yan, et al. Dissolved Gas Analysis Based QPSO-BP Model for Transformer Fault Diagnosis[J]. High voltageapparatus, 2016, 52(2): 57-61. DOI： 10.13296/j.1001-1609.hva.2016.02.009.

摘要

为了提高变压器故障诊断的准确率

提出一种基于量子粒子群优化BP神经网络（quantum particle swarm optimized BP neural network

QPSO-BP）的故障诊断模型。在该算法中

用量子位的概率幅表示种群中各粒子的当前位置

用量子旋转门实现粒子位置的移动

用量子非门进行变异操作

以获取BP神经网络的权、阈值优化参数

最终实现了变压器故障诊断模型的构建。对故障DGA样本的诊断实例表明

与粒子群优化BP网络（particle swarm optimized BP neural network

PSO-BP）法、BPNN法以及IEC三比值法相比

QPSO-BP算法具有更高的诊断正确率

从而实现了变压器故障模式的有效识别。

Abstract

In order to improve the accuracy of transformer fault diagnosis

a model is proposed based on quantum particle swarm optimized BP neural network（QPSO-BP）. In this algorithm

the current position of the particle is achieved with probability amplitude of quantum bits

quantum rotating gate is employed in moving position

then the mutation operation is performed by quantum non-gate

in order to acquire the optimization parameters of weights and thresholds of BP neural network

ultimately the transformer fault diagnosis model is established. The simulation results of DGA samples show that the QPSO-BP algorithm has higher diagnosis accuracy compared with particle swarm optimized BP neural network（PSO-BP）

BP neural network and IEC three ratio

and achieves the effective recognition of transformer failure mode.

关键词

Keywords

references

Views

259

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Power Transformer Fault Diagnosis via Neural Network Based on Particle Swarm Optimization with Neighborhood Operator

Application of Neural Network Based on Improved Particle Swarm Optimization in the Condition Monitoring of Transformer

Fault Diagnosis Model of Transformer Based on GJO Feature Optimization and AO-RF

Research on Diagnosis Method of Transformer Winding Deformation Fault Based on Dynamic Time Warping

New Approach to Transformer Fault Diagnosis Based on ANN

Related Author

JIA Rong

XU Qi-hui

LI Hui

LIU Wei

JIA Rong1

LI Hong-bin2

KANG Hui-xi2

HONG Gang1

Related Institution

College of Electrical Engineering,Xi’an University of Technology

西安理工大学电力工程系陕西西安710048

Department of Electrical Engineering,Xi′an University of Technology,Xi′an

Gansu Tou River Hydropower Development Ltd.

China Nuclear Power Engineering Co., Ltd.

⁰