期刊
IET GENERATION TRANSMISSION & DISTRIBUTION
卷 12, 期 5, 页码 1145-1152出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2017.0883
关键词
power distribution faults; fault diagnosis; neural nets; power engineering computing; discrete wavelet transforms; power supply quality; HIF identification; HIF classification; middle voltage distribution networks; modern power system operation; power quality improvements; power distribution network faults identification; power distribution network faults classification; PDN faults classification; PDN faults identification; high-impedance faults identification; high-impedance faults classification; medium-voltage PDN; voltage phase difference algorithm; discrete wavelet transform; artificial neural networks algorithm; real distribution network; Bosnia; Herzegovina
The modern power system operation is faced with numerous challenges related to the power quality improvements such as identification and classification of power distribution network (PDN) faults. The recent advances in the area of signal processing allow the development of new algorithms and methods which can be used for fault identification and classification in PDN. This study presents a comparison of two approaches for identification and classification of high-impedance faults (HIFs) in medium-voltage PDN. The first approach is based on the voltage phase difference algorithm, whereas the second approach is based on the combination of discrete wavelet transform and artificial neural networks algorithm. The proposed algorithms are tested on models of a real distribution network, which represents a typical PDN currently used in Bosnia and Herzegovina. It was demonstrated that the proposed methods are capable to accurately detect and classify HIF in PDN. This study makes a contribution to the existing body of knowledge by developing, testing and comparing two methods for HIF classification and identification, whose application represents an improvement when compared with the capability of the existing protection devices.
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