[1]付宝英,王启志.自适应粒子群优化BP神经网络的变压器故障诊断[J].华侨大学学报(自然科学版),2013,34(3):262-266.[doi:10.11830/ISSN.1000-5013.2013.03.0262]
　FU Bao-ying,WANG Qi-zhi.Transformer Fault Diagnosis of Adaptive Particle SwarmOptimization BP Neural Network[J].Journal of Huaqiao University(Natural Science),2013,34(3):262-266.[doi:10.11830/ISSN.1000-5013.2013.03.0262]
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自适应粒子群优化BP神经网络的变压器故障诊断()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第34卷
期数:
2013年第3期
页码:
262-266
栏目:
出版日期:
2013-05-20

文章信息/Info

Title:
Transformer Fault Diagnosis of Adaptive Particle SwarmOptimization BP Neural Network
文章编号:
1000-5013(2013)03-0262-05
作者:
付宝英;  王启志
华侨大学 机电及自动化学院, 福建 厦门 361021
Author(s):
FU Bao-ying;  WANG Qi-zhi
College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
关键词:
变压器;  故障诊断;  BP神经网络;  粒子群算法
Keywords:
transformer;  fault diagnosis;  BP neural network;  particle swarm algorithm
分类号:
TP183;TP301
DOI:
10.11830/ISSN.1000-5013.2013.03.0262
文献标志码:
A
摘要:
在分析粒子群参数特征的基础上,提出自适应粒子群优化算法,使用自适应粒子群优化BP神经网络,建立基于自适应粒子群优化BP神经网络(PSO-BP)的变压器故障诊断系统.通过对52组训练样本和28组测试样本的仿真实验,可知自适应PSO-BP法能提高变压器故障诊断的准确率,有效减小网络的误差精度.
Abstract:
Based on the analysis of the particle swarm parameter characteristic, adaptive particle swarm optimization algorithm is put forward. Using adaptive particle swarm optimization back propagation(PSO-BP)neural network, transformer fault diagnosis system is built up based on adaptive particle swarm optimization BP neural network. By the simulation experiment using 52 groups of training samples and 28 groups of test samples, it can be seen that the adaptive PSO-BP method can improve the transformer fault diagnosis accuracy and reduce the network error precision effectively.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2012-04-20
通信作者: 王启志(1971-),男,副研究员,主要从事先进智能控制方面的研究.E-mail:wangqz@hqu.edu.cn.
基金项目: 福建省自然科学基金资助项目(A0640004)
更新日期/Last Update: 2013-05-20