A Novel Fault Location Methodology for Smart Distribution Networks

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Abstract

Power distribution networks (PDNs) has played a crucial role in expediting transition towards cleaner and better distributed energy sources. Nowadays, more and more distributed generations (DGs) are used in PDNs which complicates the automatic fault location. This paper presents an accurate impedance-based method to determine the fault location for smart PDN in the presence of DGs. In addition, phase domain equations of distributed line parameters are used to enhance the accuracy of fault location. Two types of networks are considered. The first type of network is assumed to be fully observable with μPMU and in the second type there are only a few μPMUs with data loggers on the rest nodes. Load impedances of all nodes are estimated using pre-fault recorded information by present μPMUs and data loggers. The proposed algorithm might suggest several points as possible fault locations for a PDN. To find out the actual location of fault same fault type is simulated for all suggested points. A matching value which is mathematically defined in the paper, is calculated using recorded and simulated voltage to determine the actual fault point among all the suggested candidates. The accuracy of suggested method is analyzed against various conditions.
OriginalsprogEngelsk
TidsskriftIEEE Transactions on Smart Grid
Vol/bind12
Udgave nummer2
Sider (fra-til)1277-1288
ISSN1949-3053
DOI
StatusUdgivet - mar. 2021

Bibliografisk note

Funding Information:
This work was supported by "Smart Fault Prediction and Location for Distribution Grids" project through the Danish Energy Agency under the Energy Technology Development and Demonstration Program under Grant 64019-0592. Paper no. TSG-00142-2020.

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Finansiering

This work was supported by "Smart Fault Prediction and Location for Distribution Grids" project through the Danish Energy Agency under the Energy Technology Development and Demonstration Program under Grant 64019-0592. Paper no. TSG-00142-2020.

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