微型电脑应用2024,Vol.40Issue(1) :127-130.

柱上开关电磁辐射干扰测试技术研究

Research on Electromagnetic Radiation Interference Testing Technology of Column Switch

宋殷冠 邹宇 苏一峰 汪明 谢振俊 池小兵
微型电脑应用2024,Vol.40Issue(1) :127-130.

柱上开关电磁辐射干扰测试技术研究

Research on Electromagnetic Radiation Interference Testing Technology of Column Switch

宋殷冠 1邹宇 1苏一峰 1汪明 1谢振俊 1池小兵1
扫码查看

作者信息

  • 1. 广西电网有限责任公司钦州供电局,广西,钦州 535000
  • 折叠

摘要

针对现有技术中柱上开关电磁辐射干扰测试能力滞后等问题,研究通过搭建电磁辐射对柱上开关干扰测试装置,通过柱上开关集合、屏蔽模块、检测模块、电磁干扰信号发生器、电压检测、电流检测、信号处理、干扰信号分析模块以及终端分析模块等实现柱上开关电磁辐射干扰测试;通过构建一种新型的改进深度学习模型,应用RBF神经网络,大大提高柱上开关电磁辐射干扰的预测精度.通过实验,这种方法提高了柱上开关电磁辐射干扰预测能力.

Abstract

In view of the lag of electromagnetic radiation interference test ability of column switch in the prior art,the research realizes the electromagnetic radiation interference test of column switch by building the electromagnetic radiation interference test device of column switch,and realizes the electromagnetic radiation interference test of column switch through column switch collection,shielding module,detection module,electromagnetic interference signal generator,voltage detection,current detection,signal processing,interference signal analysis module and terminal analysis module.By constructing a new improved deep learning model and applying RBF neural network,the prediction accuracy of electromagnetic radiation interference of col-umn switch is greatly improved.Through experiments,the research method improves the prediction ability of electromagnetic radiation interference of column switch.

关键词

柱上开关/电磁辐射/深度学习模型/RBF神经网络/干扰测试/预测精度

Key words

column switch/electromagnetic radiation/deep learning model/RBF neural network/interference test/prediction accuracy

引用本文复制引用

出版年

2024
微型电脑应用
上海市微型电脑应用学会

微型电脑应用

CSTPCD
影响因子:0.359
ISSN:1007-757X
参考文献量10
段落导航相关论文