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Characteristics and classification of high‐current fault arcs on distribution systems
Author(s) -
Goda Yutaka,
Okazaki Masayuki,
Inaba Tsuginori
Publication year - 1996
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.4391160606
Subject(s) - overvoltage , waveform , fault (geology) , electric arc , arc (geometry) , voltage , electrical engineering , artificial neural network , arc fault circuit interrupter , engineering , electronic engineering , acoustics , computer science , mechanical engineering , artificial intelligence , physics , electrode , geology , seismology , short circuit , quantum mechanics
When a fault occurs on transmission or distribution systems due to lightning or overvoltage, often an arc discharge occurs at the fault point. The arc discharge, which is caused by a fault current, has a high current, high temperature, strong light emission, etc., thus it sometimes causes heavy damages to electric power equipment. The arc discharge is influenced by the conditions around the arcs, i.e., gas, insulation materials, gap length, weather, etc. Also, the arc voltage along the arc column indicates the characteristics of the arc. If the voltage waveforms of the arcs caused by the fault on transmission or distribution systems are classified, it is possible to find the location and the equipment where the fault occurred. In this paper, the arc voltage data in 6‐kV class XLPE cables and 6‐kV class overhead lines are analyzed and an artificial neural network method is applied to classify the arc voltage waveforms. The results obtained from the six artificial neural networks developed show that the artificial neural network method is effective for classification of arc voltage waveforms if adequate input parameters are selected.