|Table of Contents|

 Temporary overvoltage classification and recognition method of distribution network based on LCD-Hilbert transform and
singular-spectrum entropy
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[ISSN:1007-449X/CN:23-1 408/TM]

Issue:
2018年11
Page:
26-36
Research Field:
Publishing date:

Info

Title:
 Temporary overvoltage classification and recognition method of distribution network based on LCD-Hilbert transform and
singular-spectrum entropy
Author(s):
 JIN Tao1 XU Li-bin1 GAO Wei1 GUO Mou-fa1 CHEN Yong-wang2
 ( 1. College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350116,China;2. State Grid Fujian Jinjiang County Electric Power Supply Co. ,Ltd. ,Quanzhou 362200,China)
Keywords:
neutral isolated power system temporary overvoltage energy contribution rate mean valuesingular spectrum entropy center gravity frequency band
PACS:
TM 73
DOI:
10.15938/j.emc.2018.11.004
Abstract:
  In view of the problem of overvoltage identification in the current distribution network of power system,a temporary overvoltage classification and recognition method of distribution network based on time-frequency distribution characteristics was proposed. In neutral isolated power system,the threephase voltage of bolted single-phase ground overvoltage has the lowest energy distribution uniformity; the zero-sequence voltage of intermittent arc grounding overvoltage has the highest DC component; and the zero-
sequence voltage of different kinds of temporary overvoltage has great differences on amplitude and frequency concentration band. The energy contribution rate of the zero-sequence voltage,the mean value of the zero-sequence voltage,and the singular spectrum entropy were calculated to extract the time-domain energy distribution features. Local characteristic-scale decomposition( LCD) ,Hilbert transform and the band-pass filter algorithm were adopted to calculate the center gravity frequency band,so as to extract the
frequency-domain energy distribution features. Finally,the types of temporary overvoltage were recognized by the threshold discrimination method. The method does not need classifier,and it has the advantages of simple algorithm and little computation time. Under different fault conditions,higher recognition rate was obtained both in simulation and reality testing.

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Last Update: 2018-12-03