| 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 |
| 混合神经网络在变压器故障诊断中的应用 |
| Application of mixed neural network in transformer fault diagnosis |
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| DOI：10.13382/j.jemi.2017.01.017 |
| 中文关键词: 故障诊断 PSO算法 SOM神经网络算法 LVQ神经网络算法 |
| 英文关键词:fault diagnosis PSO algorithm SOM neural network algorithm LVQ neural network algorithm |
| 基金项目:上海市“科技创新行动计划”社会发展领域项目（16DZ1202500）、上海市青年科技英才扬帆计划（16YF1404700）、上海市"科技创新行动计划"高新技术领域科研项目（15111106800）、上海市发电过程智能管控工程技术研究中心项目（14DZ2251100）资助 |
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| Author | Institution |
| Xia Fei | 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 |
| Luo Zhijiang | 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 |
| Zhang Hao | 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 |
| Peng Daogang | 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 |
| Zhang Qian | College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China |
| Tang Yiwen | College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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. |
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