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一种谐振接地系统电弧高阻接地故障选线新方法及仿真
引用本文:李震球,王时胜,吴丽娜.一种谐振接地系统电弧高阻接地故障选线新方法及仿真[J].电力系统保护与控制,2014,42(17):44-49.
作者姓名:李震球  王时胜  吴丽娜
作者单位:南昌大学信息工程学院,江西 南昌 330031;南昌大学信息工程学院,江西 南昌 330031;南昌大学信息工程学院,江西 南昌 330031
基金项目:江西省科技支撑计划项目(2010BSA02500)
摘    要:提出了一种电弧高阻模型,并针对谐振接地系统电弧高阻接地故障选线问题,提出一种基于希尔伯特-黄变换和概率神经网络的选线新方法。采集每条线路在单相接地故障发生时的零序电流暂态信号(一个周波,故障前1/4个周波和故障后3/4个周波)进行EMD分解,计算各条线路的特征固有模态能量。将各条线路的特征固有模态能量输入到PNN网络,实现故障线路自动识别。运用Matlab软件对电弧高阻接地故障、混合线缆系统接地故障分别进行了选线仿真,并进行了噪声干扰试验,验证了该方法的可行性与准确性。

关 键 词:谐振接地系统  故障选线  电弧高阻接地故障  概率神经网络  希尔伯特-黄变换
收稿时间:2013/11/15 0:00:00
修稿时间:2013/12/30 0:00:00

A new method and simulation for arcing high-impedance-grounding fault line selection in resonant grounded system
LI Zhen-qiu,WANG Shi-sheng,and WU Li-na.A new method and simulation for arcing high-impedance-grounding fault line selection in resonant grounded system[J].Power System Protection and Control,2014,42(17):44-49.
Authors:LI Zhen-qiu  WANG Shi-sheng  and WU Li-na
Affiliation:Information Engineering College, Nanchang University, Nanchang 330031, China;Information Engineering College, Nanchang University, Nanchang 330031, China;Information Engineering College, Nanchang University, Nanchang 330031, China
Abstract:An arcing high-impedance model is presented. An intelligent new approach for arcing high-impedance-grounded fault detection in resonant grounded system is presented conbined Hilbert-Huang transform (HHT) with probabilistic neural network (PNN). Zero-sequence current (including the faulted cycle, the 1/4 before fault and the 3/4 after fault) of each circuit caused by single-phase grounded fault is collected, and then the wave is analyzed by EMD and the characteristic of the intrinsic modal energy is calculated. Then the intrinsic modal energy is put into the PNN network. In this way, fault line selection is achieved automatically. Arcing high-impedance-grounded fault and hybrid cable system grounded fault are simulated by MATLAB in this method and noise interference experiment proves the method is feasible and accurate.
Keywords:resonant grounded system  fault line selection  arcing high-impedance-grounded fault  probabilistic neural network  Hilbert-Huang transform
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