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.
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)
出处
《微型电脑应用》
2024年第1期127-130,共4页
Microcomputer Applications
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
column switch
electromagnetic radiation
deep learning model
RBF neural network
interference test
prediction accuracy