Fault Diagnosis of Transformer Based on BP Neural Network and ACS-SA

LI Xiaozhu; CHEN Zhijun; FAN Xiaochao; XU Qidan; LU Jian; HE Teng

doi:10.13296/j.1001-1609.hva.2018.02.022

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Fault Diagnosis of Transformer Based on BP Neural Network and ACS-SA

更新时间：2025-04-26

- Fault Diagnosis of Transformer Based on BP Neural Network and ACS-SA
- High VoltageApparatus Vol. 54, Issue 2, Pages: 134-139+146(2018)
- 作者机构：
  
  新疆大学
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2018.02.022
  CLC： TM41
- Published：2018
- 稿件说明：
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[1]李笑竹,陈志军,樊小朝,徐其丹,鹿剑,何腾.基于ACS-SA文化基因算法的BP神经网络变压器故障诊断[J].高压电器,2018,54(02):134-139+146.

LI Xiaozhu, CHEN Zhijun, FAN Xiaochao, et al. Fault Diagnosis of Transformer Based on BP Neural Network and ACS-SA[J]. High voltageapparatus, 2018, 54(2): 134-139+146.
[1]李笑竹,陈志军,樊小朝,徐其丹,鹿剑,何腾.基于ACS-SA文化基因算法的BP神经网络变压器故障诊断[J].高压电器,2018,54(02):134-139+146. DOI： 10.13296/j.1001-1609.hva.2018.02.022.

LI Xiaozhu, CHEN Zhijun, FAN Xiaochao, et al. Fault Diagnosis of Transformer Based on BP Neural Network and ACS-SA[J]. High voltageapparatus, 2018, 54(2): 134-139+146. DOI： 10.13296/j.1001-1609.hva.2018.02.022.

摘要

针对BP神经网络在变压器故障诊断上存在的不足

提出基于ACS-SA文化基因算法的BP神经网络变压器故障诊断方法。在实际系统中

针对缺乏准确的变量参数估计

将边界变异策略和自适应步长策略引入标准布谷鸟算法中;提出一种在改进的布谷鸟算法中结合局部搜索策略的文化基因算法;建立BP神经网络变压器故障诊断模型

并用文化基因布谷鸟算法优化BP神经网络的权值和阈值。仿真实验及对比研究结果表明

该算法能准确有效地识别变压器的故障类型

较其他算法（CS-BP神经网络算法和POS-BP神经网络算法）有更高的准确率

为变压器故障诊断提供一种新思路。

Abstract

Aiming at the deficiency of BP neural network in the fault diagnosis of transformer

to tackle this prob-lem

a BP neural network transformer fault diagnosis method based on ACS-SA culture genetic algorithm is pro-posed. In the practical system

in view of the lack of accurate variable parameter estimation

the boundary mutationstrategy and adaptive step strategy are introduced to the standard cuckoo algorithm.Puts forward a cultural genetic al-gorithm combining improved cuckoo algorithm and local search strategy. Establish the fault diagnosis model of trans-former based on BP neural network

and cultural gene cuckoo algorithm is used to optimize the weights and thresh-olds of BP neural network. The final findings show that

this method is accurate and effective in identifying the faulttype of the transformer

and compare with other algorithms（CS-BP neural network algorithm and POS-BP neural net-work algorithm）

the method has a higher accuracy

as well as provides a new way of thinking for transformer fault di-agnosis.

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