Insulator Pollution Monitoring Based on Acoustic Signal and One-Dimensional Convolutional Neural Network

LI Zhenhua; LI Hao; HUANG Jingguang; ZHANG Lei; WU Lin

doi:10.3969/j.issn.1008-0198.2022.04.004

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Insulator Pollution Monitoring Based on Acoustic Signal and One-Dimensional Convolutional Neural Network

更新时间：2025-06-11

- Insulator Pollution Monitoring Based on Acoustic Signal and One-Dimensional Convolutional Neural Network
- Vol. 42, Issue 4, Pages: 18-22(2022)
- 作者机构：
  
  1. 三峡大学,湖北,宜昌,443002
  2. 国网湖北省电力有限公司技术培训中心,湖北,武汉,430014
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2022.04.004
  CLC： TM216.4
- Published：2022
- 稿件说明：
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李振华, 李浩, 黄景光, 张磊, 吴琳. 基于声信号和一维卷积神经网络的绝缘子污秽度监测方法研究[J]. 湖南电力, 2022, 42(4): 18-22.

LI Zhenhua, LI Hao, HUANG Jingguang, et al. Insulator Pollution Monitoring Based on Acoustic Signal and One-Dimensional Convolutional Neural Network[J]. 2022, 42(4): 18-22.
李振华, 李浩, 黄景光, 张磊, 吴琳. 基于声信号和一维卷积神经网络的绝缘子污秽度监测方法研究[J]. 湖南电力, 2022, 42(4): 18-22. DOI： 10.3969/j.issn.1008-0198.2022.04.004.

LI Zhenhua, LI Hao, HUANG Jingguang, et al. Insulator Pollution Monitoring Based on Acoustic Signal and One-Dimensional Convolutional Neural Network[J]. 2022, 42(4): 18-22. DOI： 10.3969/j.issn.1008-0198.2022.04.004.

摘要

针对高压输电线路污秽绝缘子放电模式监测

设计一种新型的一维卷积神经网络结构(1D-CNN)

提出一种基于声发射信号和1D-CNN的污秽绝缘子放电模式监测方法。将实验室采集到的不同放电状态下的声发射信号经过预处理后

利用卷积神经网络对放电信号样本进行自适应特征提取和特征降维

以减少训练模型参数和计算量

最终使用Softmax函数对预测结果进行分类。识别结果表明模型能够达到99.84%以上的识别率

减少了传统绝缘子污秽度监测方法中人工对数据进行预处理的过程

可有效应用于污秽绝缘子放电模式监测任务。

Abstract

Aiming at the problem of discharge mode monitoring of polluted insulators in high voltage transmission lines

a new one-dimensional convolutional neural network structure (1D-CNN) is designed for monitoring the discharge patterns of fouled insulators in high-voltage lines

and a fouled insulator discharge pattern monitoring method based on acoustic emission signals and 1D-CNN is proposed. After preprocessing the acoustic emission signals collected in the laboratory under different discharge states

the convolutional neural network is used to perform adaptive feature extraction and feature dimensionality reduction on the discharge signal samples to reduce the training model parameters and computation

and finally the Softmax function is used to classify the prediction results. The recognition results show that the model can achieve a recognition rate of more than 99.84%

which effectively solves the process of manual preprocessing of data in the traditional insulator fouling degree monitoring method and can be effectively applied to the task of fouling insulator discharge pattern monitoring.

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references

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