Fault location for medium voltage underground cables using Bayesian inference

Y. Xiang; J.F.G. Cobben

doi:10.1109/PTC.2015.7232456

Fault location for medium voltage underground cables using Bayesian inference

Y. Xiang, J.F.G. Cobben

Electrical Energy Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

Abstract

This paper proposes a method for fault location in medium voltage grids with underground cables, using Bayesian inference. The prior distribution of fault position is obtained from the component failure database. With the measured fault loop impedance, the posterior distribution is calculated based on Bayes' theorem. Several random factors are considered during the calculation, including measurement errors, fault breakdown resistance, and inaccuracies of zero-sequence parameters. The distribution grid operators can use the calculated posterior distribution to rank the possible faulted sections and facilitate the restoration process, which can reduce the interruption duration of power supply.

Original language	English
Title of host publication	Proceedings of PowerTech, 2015 IEEE Eindhoven, June 29 2015-July 2 2015, Eindhoven, The Netherlands
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	1-5
Number of pages	5
DOIs	https://doi.org/10.1109/PTC.2015.7232456
Publication status	Published - 2 Jul 2015
Event	2015 IEEE Power Tech - Eindhoven, Netherlands Duration: 29 Jun 2015 → 2 Jul 2015 http://powertech2015-eindhoven.tue.nl/

Conference

Conference	2015 IEEE Power Tech
Country/Territory	Netherlands
City	Eindhoven
Period	29/06/15 → 2/07/15
Internet address	http://powertech2015-eindhoven.tue.nl/

Keywords

Bayesian inference
fault location
fault loop impedance
underground cable

Access to Document

10.1109/PTC.2015.7232456

Cite this

@inproceedings{dce69eff2f41403d9ab0e949bfc16a5b,

title = "Fault location for medium voltage underground cables using Bayesian inference",

abstract = "This paper proposes a method for fault location in medium voltage grids with underground cables, using Bayesian inference. The prior distribution of fault position is obtained from the component failure database. With the measured fault loop impedance, the posterior distribution is calculated based on Bayes' theorem. Several random factors are considered during the calculation, including measurement errors, fault breakdown resistance, and inaccuracies of zero-sequence parameters. The distribution grid operators can use the calculated posterior distribution to rank the possible faulted sections and facilitate the restoration process, which can reduce the interruption duration of power supply.",

keywords = "Bayesian inference, fault location, fault loop impedance, underground cable",

author = "Y. Xiang and J.F.G. Cobben",

year = "2015",

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doi = "10.1109/PTC.2015.7232456",

language = "English",

pages = "1--5",

booktitle = "Proceedings of PowerTech, 2015 IEEE Eindhoven, June 29 2015-July 2 2015, Eindhoven, The Netherlands",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2015 IEEE Power Tech ; Conference date: 29-06-2015 Through 02-07-2015",

url = "http://powertech2015-eindhoven.tue.nl/",

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Xiang, Y & Cobben, JFG 2015, Fault location for medium voltage underground cables using Bayesian inference. in Proceedings of PowerTech, 2015 IEEE Eindhoven, June 29 2015-July 2 2015, Eindhoven, The Netherlands. Institute of Electrical and Electronics Engineers, Piscataway, pp. 1-5, 2015 IEEE Power Tech, Eindhoven, Netherlands, 29/06/15. https://doi.org/10.1109/PTC.2015.7232456

Fault location for medium voltage underground cables using Bayesian inference. / Xiang, Y.; Cobben, J.F.G.

Proceedings of PowerTech, 2015 IEEE Eindhoven, June 29 2015-July 2 2015, Eindhoven, The Netherlands. Piscataway : Institute of Electrical and Electronics Engineers, 2015. p. 1-5.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Fault location for medium voltage underground cables using Bayesian inference

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AU - Cobben, J.F.G.

PY - 2015/7/2

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N2 - This paper proposes a method for fault location in medium voltage grids with underground cables, using Bayesian inference. The prior distribution of fault position is obtained from the component failure database. With the measured fault loop impedance, the posterior distribution is calculated based on Bayes' theorem. Several random factors are considered during the calculation, including measurement errors, fault breakdown resistance, and inaccuracies of zero-sequence parameters. The distribution grid operators can use the calculated posterior distribution to rank the possible faulted sections and facilitate the restoration process, which can reduce the interruption duration of power supply.

AB - This paper proposes a method for fault location in medium voltage grids with underground cables, using Bayesian inference. The prior distribution of fault position is obtained from the component failure database. With the measured fault loop impedance, the posterior distribution is calculated based on Bayes' theorem. Several random factors are considered during the calculation, including measurement errors, fault breakdown resistance, and inaccuracies of zero-sequence parameters. The distribution grid operators can use the calculated posterior distribution to rank the possible faulted sections and facilitate the restoration process, which can reduce the interruption duration of power supply.

KW - Bayesian inference

KW - fault location

KW - fault loop impedance

KW - underground cable

U2 - 10.1109/PTC.2015.7232456

DO - 10.1109/PTC.2015.7232456

M3 - Conference contribution

SP - 1

EP - 5

BT - Proceedings of PowerTech, 2015 IEEE Eindhoven, June 29 2015-July 2 2015, Eindhoven, The Netherlands

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 2015 IEEE Power Tech

Y2 - 29 June 2015 through 2 July 2015

ER -

Fault location for medium voltage underground cables using Bayesian inference

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Keywords

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