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Generalized Approach for Fault Detection in Medium Voltage Distribution Networks Based on Magnetic Field Measurement

Noninvasive contactless methods for electric power line monitoring based on magnetic field measurement have become an interesting topic for researchers and the electric power industry since introduction of the Smart Grid concept. By measuring and analyzing magnetic field originating from currents in...

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Bibliographic Details
Published in:IEEE transactions on power delivery 2020-06, Vol.35 (3), p.1189-1199
Main Authors: Lekic, Dorde M., Mrsic, Predrag D., Erceg, Bojan B., Zeljkovic, Cedomir V., Kitic, Nemanja S., Matic, Petar R.
Format: Article
Language:English
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Summary:Noninvasive contactless methods for electric power line monitoring based on magnetic field measurement have become an interesting topic for researchers and the electric power industry since introduction of the Smart Grid concept. By measuring and analyzing magnetic field originating from currents in power line conductors, it is possible to detect faults in the network. In medium voltage distribution networks, where a variety of different pole geometries are present, different criteria for fault detection have to be employed for each geometry, which complicates detection and influences accuracy. This paper proposes a novel approach for fault detection in medium voltage distribution networks which is based on processing of signals measured by low cost contactless magnetic field sensors. In order to create a generalized method for fault detection, a sequence of mathematical transformations of the measured magnetic field components is applied. A novel geometric transformation which eliminates influence of pole geometry is introduced, providing signals from which steady-state symmetrical components of the rotating magnetic field are computed. Those components are used as general fault detection criteria. The proposed approach is confirmed to be applicable for different fault types by a set of experiments on three-phase overhead power line model scaled to laboratory conditions.
ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2019.2935572