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作 者:周晓琴 金祝飞 刘海林 ZHOU Xiaoqin;JIN Zhufei;LIU Hailin(Jiaxing Electric Power Supply Company of State Grid Zhejiang Electric Power Compary,Jiaxing 314000,China)
出 处:《微型电脑应用》2024年第10期109-111,125,共4页Microcomputer Applications
摘 要:为了提升行波故障识别效果,降低误报率与虚警率,提出一种基于北斗授时数字技术的配电网行波故障隐患识别方法。采集行波故障信号,采用北斗授时数字技术将采集到的信号按照时间顺序进行记录和分析。通过带通滤波器消除其他频率干扰,突出故障信号的特征。针对行波故障信号数字滤波结果,使用递归神经网络构建故障隐患识别模型,运用该模型实现行波故障隐患识别。实验结果表明,所提方法的误报率与虚警率较低,且能够在噪声状态下获取信号时频特征,实现对多通道信号的准确识别,有利于提高配电系统的可靠性和稳定性。In order to improve the recognition effect of traveling wave faults and reduce the false alarm rate and false alarm rate,a distribution network traveling wave fault hidden danger identification method based on Beidou time service digital technology is proposed.It collects traveling wave fault signals and uses Beidou time service digital technology to record and analyze the col-lected signals in chronological order.Other frequency interferences are eliminated through bandpass filters and the characteris-tics of fault signals are highlighted.Based on the digital filtering results of traveling wave fault signals,a recursive neural net-work is used to construct a fault hidden danger identification model,and this model is used to achieve traveling wave fault hid-den danger identification.The experimental results show that the proposed method has a low false alarm rate and false alarm rate,and can obtain the time-frequency characteristics of the signal under noisy conditions,achieve accurate recognition of multi-channel signals,which is conducive to improving the reliability and stability of the distribution system.
分 类 号:TM726.1[电气工程—电力系统及自动化]
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