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混合神经网络在变压器故障诊断中的应用
引用本文:夏飞,罗志疆,张浩,彭道刚,张茜,唐依雯.混合神经网络在变压器故障诊断中的应用[J].电子测量与仪器学报,2017,31(1):118-124.
作者姓名:夏飞  罗志疆  张浩  彭道刚  张茜  唐依雯
作者单位:1. 同济大学电子与信息工程学院 上海201804;上海电力学院 自动化工程学院 上海200090;2. 上海电力学院 自动化工程学院 上海200090;上海发电过程智能管控工程技术研究中心 上海200090;3. 上海电力学院 自动化工程学院 上海200090
基金项目:上海市“科技创新行动计划”社会发展领域项目,上海市青年科技英才扬帆计划,上海市“科技创新行动计划”高新技术领域科研项目,上海市发电过程智能管控工程技术研究中心项目
摘    要:针对变压器故障诊断准确率低的问题提出了粒子群-自组织映射-学习矢量化(PSO-SOM-LVQ)混合神经网络算法。为了获取更加有效的SOM神经网络拓扑结构,首先采用PSO算法对SOM神经网络的权值向量加以改进,在此基础上融入LVQ神经网络,弥补了无监督学习SOM神经网络的不足。这种PSO、SOM和LVQ相结合的混合神经网络算法提高了变压器故障诊断的精度,减少了故障诊断的误差。通过仿真,对SOM、PSO-SOM和PSO-SOM-LVQ这3种算法进行了对比。对比结果表明,PSO-SOM-LVQ混合神经网络算法准确度最高,其故障诊断准确率为100%。由此可见,采用PSO-SOM-LVQ混合神经网络算法可有效提高变压器故障诊断的性能。

关 键 词:故障诊断  PSO算法  SOM神经网络算法  LVQ神经网络算法

Application of mixed neural network in transformer fault diagnosis
Xia Fei,Luo Zhijiang,Zhang Hao,Peng Daogang,Zhang Qian and Tang Yiwen.Application of mixed neural network in transformer fault diagnosis[J].Journal of Electronic Measurement and Instrument,2017,31(1):118-124.
Authors:Xia Fei  Luo Zhijiang  Zhang Hao  Peng Daogang  Zhang Qian and Tang Yiwen
Affiliation:1. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;2. College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China,2. College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China; 3. Shanghai Engineering Research Center of Intelligent Management and Control for Power Process, Shanghai 200090, China,1. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;2. College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China,2. College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China; 3. Shanghai Engineering Research Center of Intelligent Management and Control for Power Process, Shanghai 200090, China,College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China and College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Abstract:Aiming at the shortcoming of the low accuracy of transformer fault diagnosis,the PSO-SOM-LVQ (particle swarm optimization,self organizing maps,learning vector quantization) mixed neural network algorithm is presented in this paper.Firstly,the weight of SOM neural network is optimized by the method of PSO algorithm to obtain the more effective topology.Based on that,LVQ neural network is combined to cover the shortage of unsupervised learning SOM neural network.The mixed neural network algorithm combined with PSO,SOM and LVQ can improve the accuracy and reduce the error of transformer fault diagnosis.Through simulation,the three algorithms of SOM,PSO-SOM and PSO-SOM-LVQ are compared.The comparison result show that the PSO-SOM-LVQ mixed neural network algorithm has the highest accuracy,and the fault diagnosis accuracy rate is 100%.Thus it can be seen,the PSO-SOM-LVQ mixed neural network algorithm can enhance the performance of transformer fault diagnosis effectively.
Keywords:fault diagnosis  PSO algorithm  SOM neural network algorithm  LVQ neural network algorithm
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