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

作　　者：宋殷冠 邹宇 苏一峰 汪明 谢振俊 池小兵 SONG Yinguan;ZOU Yu;SU Yifeng;WANG Ming;XIE Zhenjun;CHI Xiaobing(Qinzhou Power Supply Bureau of Guangxi Power Grid Co.,Ltd.,Qinzhou 535000,China)

机构地区：[1]广西电网有限责任公司钦州供电局,广西钦州535000

出　　处：《微型电脑应用》2024年第1期127-130,共4页Microcomputer Applications

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。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 column 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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TM937[自动化与计算机技术—计算机科学与技术] TM930.115[电气工程—电力电子与电力传动]