基于IAO-VMD与SO-PNN的输电线路故障诊断方法

卢万杰; 马磐

doi:10.13296/j.1001-1609.hva.2026.07.020

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基于IAO-VMD与SO-PNN的输电线路故障诊断方法

研究与分析 | 更新时间：2026-07-06

- 基于IAO-VMD与SO-PNN的输电线路故障诊断方法
- Transmission Line Fault Diagnosis Method Based on IAO-VMD and SO-PNN
- 高压电器 2026年62卷第7期页码：193-203
- 作者机构：
  
  辽宁工程技术大学，辽宁葫芦岛 125105
- 作者简介：
  
  卢万杰（1979—），女，博士，副教授，研究方向为人工智能控制。
  马磐（1999—），男，硕士研究生，研究方向为电力系统故障诊断（通信作者）（E-mail:1326309303@qq.com）。
- 基金信息：
  
  国家自然科学基金项目;National Natural Science Foundation of China(51974151)
- DOI：10.13296/j.1001-1609.hva.2026.07.020
  中图分类号：
- 收稿：2025-12-19，
  
  修回：2026-02-07，
  
  纸质出版：2026-07-16
- 稿件说明：
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卢万杰, 马磐. 基于IAO-VMD与SO-PNN的输电线路故障诊断方法[J]. 高压电器, 2026,62(7):193-203.

LU Wanjie, MA Pan. Transmission Line Fault Diagnosis Method Based on IAO-VMD and SO-PNN[J]. High Voltage Apparatus, 2026, 62(7): 193-203.
卢万杰, 马磐. 基于IAO-VMD与SO-PNN的输电线路故障诊断方法[J]. 高压电器, 2026,62(7):193-203. DOI： 10.13296/j.1001-1609.hva.2026.07.020.

LU Wanjie, MA Pan. Transmission Line Fault Diagnosis Method Based on IAO-VMD and SO-PNN[J]. High Voltage Apparatus, 2026, 62(7): 193-203. DOI： 10.13296/j.1001-1609.hva.2026.07.020.

摘要

针对输电线路中短路故障诊断识别率较低的问题，文中提出了一种基于改进天鹰优化算法（improved aquila optimization algorithm）优化VMD与蛇算法（snake optimizer

SO）优化概率神经网络（probabilistic neural network）相结合的输电线路故障诊断方法。首先利用MATLAB/Simulink软件搭建输电线路的仿真模型，分别模拟不同类型的短路故障，并选取三相故障电压信号作为故障源。然后采用singer混沌映射、反向学习、分段式非线性递减权重和柯西变异等策略对天鹰算法进行改进。接着利用改进后的天鹰优化算法对VMD两个影响因子进行参数寻优。最后利用优化后的VMD对三相故障电压信号进行分解，并计算各分量的样本熵值，构建成输电线路的故障特征集，将故障特征集划分成训练集与测试集输入到SO-PNN的识别模型中进行故障诊断。并分别与PNN、DELM等故障诊断模型进行对比。通过仿真实验可知，文中提出的诊断模型可以更加准确地完成诊断，诊断率高达93%以上，具有较好的故障识别能力。

Abstract

To address the problem of low recognition rate of short circuit fault diagnosis in transmission lines

this paper proposes a transmission line fault diagnosis method based on the combination of improved aquila optimization algorithm optimized VMD and snake algorithm optimized probabilistic neural network. First

a simulation model of the transmission line is set up usingMATLAB/Simulink software to simulate different types of short circuit faults respectively

and the three-phase fault voltage signal is selected as the fault source. Then

such strategies as singer chaotic mapping

reverse learning

segmented nonlinear decreasing weights

and Cauchy mutation are used to improve the Aquila algorithm. After that

the improved Aquila optimization algorithm is used to optimize the parameters of the two influencing factors of VMD. Finally

the optimized VMD is used to decompose the three-phase fault voltage signal and calculate the sample entropy values of each component so to construct a fault feature set for the transmission line. The fault feature set is divided into training and testing sets

input into the SO-PNN recognition model for fault diagnosis and compared with such fault diagnosis models as PNN and DELM respectively. It can be seen through simulation experiments that the diagnostic model proposed in the paper can more accurately complete the diagnosis with a diagnosis rate up to 93%

exhibiting good fault recognition ability.

关键词

Keywords

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