DocumentCode :
1761007
Title :
High-Impedance Fault Detection in the Distribution Network Using the Time-Frequency-Based Algorithm
Author :
Ghaderi, Amin ; Mohammadpour, Hossein Ali ; Ginn, Herbert L. ; Yong-June Shin
Author_Institution :
Dept. of Electr. Eng., Univ. of South Carolina, Columbia, SC, USA
Volume :
30
Issue :
3
fYear :
2015
fDate :
42156
Firstpage :
1260
Lastpage :
1268
Abstract :
A new high-impedance fault (HIF) detection method using time-frequency analysis for feature extraction is proposed. A pattern classifier is trained whose feature set consists of current waveform energy and normalized joint time-frequency moments. The proposed method shows high efficacy in all of the detection criteria defined in this paper. The method is verified using real-world data, acquired from HIF tests on three different materials (concrete, grass, and tree branch) and under two different conditions (wet and dry). Several nonfault events, which often confuse HIF detection systems, were simulated, such as capacitor switching, transformer inrush current, nonlinear loads, and power-electronics sources. A new set of criteria for fault detection is proposed. Using these criteria, the proposed method is evaluated and its performance is compared with the existing methods. These criteria are accuracy, dependability, security, safety, sensibility, cost, objectivity, completeness, and speed. The proposed method is compared with the existing methods, and it is shown to be more reliable and efficient than its existing counterparts. The effect of choice of the pattern classifier on method efficacy is also investigated.
Keywords :
capacitor switching; fault diagnosis; feature extraction; pattern classification; power distribution faults; power transformers; principal component analysis; time-frequency analysis; accuracy; capacitor switching; completeness; cost; current waveform energy; dependability; distribution network; feature extraction; feature set; high-impedance fault detection method; nonlinear loads; normalized joint time-frequency moments; objectivity; pattern classifier; power-electronics sources; principal component analysis; safety; security; sensibility; speed; statistical joint moment; time-frequency-based algorithm; transformer inrush current; Circuit faults; Feature extraction; Impedance; Joints; Surface impedance; Time-frequency analysis; Vegetation; High-impedance fault (HIF); power distribution faults; principal component analysis; protection; statistical joint moment; time-frequency analysis;
fLanguage :
English
Journal_Title :
Power Delivery, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8977
Type :
jour
DOI :
10.1109/TPWRD.2014.2361207
Filename :
6915897
Link To Document :
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