| 付 华,金 岑.VMD-PE协同SNN的输电线路故障辨识方法[J].电子测量与仪器学报,2020,34(6):86-92 |
| VMD-PE协同SNN的输电线路故障辨识方法 |
| Fault identification method of transmission lines based on VMD-PE and SNN |
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| DOI： |
| 中文关键词: 输电线路 故障辨识 变分模态分解(VMD) 孪生神经网络(SNN) 排列熵(PE) |
| 英文关键词:transmission lines fault identification variational modal decomposition ( VMD) siamese neural networks ( SNN) permutation entropy(PE) |
| 基金项目:国家自然科学基金(51974151,71771111)资助项目 |
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| 中文摘要: |
| 针对输电线路短路故障危害大、故障辨识率较低等问题,提出一种结合变分模态分解排列熵(VMD-PE)与孪生神经网
络(SNN)的故障辨识方法,利用瞬时频率均值对 VMD 进行参数优化,确定分解层数 K,通过 VMD 分解故障时的三相电压,计算
分解后每个分量的排列熵,将其作为故障特征量;将故障特征输入到训练好的 SNN 中进行相似性度量,比较两个输入样本之间
的相似程度,判别出输电线路短路故障类型。 通过仿真实验验证了该方法的可行性,并与其他分类方法相对比,证明了该方法
的准确性和优越性。 |
| 英文摘要: |
| Aiming at the problems of damage caused by short-circuit faults and low fault identification rate of transmission lines, a fault
identification method combining VMD-PE and siamese neural networks ( SNN ) is proposed. For determining the number of
decomposition layers K, use the instantaneous frequency mean to optimize VMD parameters, decompose the three-phase voltage at fault
by VMD, calculate the permutation entropy of each component after decomposition, and use them as the fault features; input the fault
features into the trained SNN for similarity measurement, compare the similarity between the two input samples to determine the type of
short-circuit fault on the transmission line. The feasibility of the method is verified by simulation experiments, and compared with other
classification methods, the accuracy and superiority of the method are proved. |
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