An online condition monitoring thermal prognostic indicator system for MV cable circuits
An online condition monitoring thermal prognostic indicator system for MV cable circuits
Developing a reliable online condition monitoring prognostic indicator tool for MV cables is of great importance as it can predict and prevent upcoming failures of the distribution cable circuits. This paper introduces a thermal prognostic model for MV underground cable terminations based on a support vector regression algorithm. The model is shown to predict the likely temperature along the cable thirty minutes into the future and is able to rapidly identify temperature anomalies which may indicate upcoming failures.
condition monitoring, support vector regression, diagnostic, prognostic, insulation system
978-1-4799-7354-5
535-538
Christou, S.
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Lewin, P.L.
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Swingler, S.G.
4f13fbb2-7d2e-480a-8687-acea6a4ed735
Pilgrim, J.A.
4b4f7933-1cd8-474f-bf69-39cefc376ab7
7 June 2015
Christou, S.
aa6be8f1-94a8-469c-84b1-ac90fcbf4a46
Lewin, P.L.
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Pilgrim, J.A.
4b4f7933-1cd8-474f-bf69-39cefc376ab7
Swingler, S.G.
4f13fbb2-7d2e-480a-8687-acea6a4ed735
Christou, S., Lewin, P.L. and Swingler, S.G.
(2015)
An online condition monitoring thermal prognostic indicator system for MV cable circuits.
Pilgrim, J.A.
(ed.)
2015 IEEE Electrical Insulation Conference (EIC), Seattle, United States.
07 - 10 Jun 2015.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Developing a reliable online condition monitoring prognostic indicator tool for MV cables is of great importance as it can predict and prevent upcoming failures of the distribution cable circuits. This paper introduces a thermal prognostic model for MV underground cable terminations based on a support vector regression algorithm. The model is shown to predict the likely temperature along the cable thirty minutes into the future and is able to rapidly identify temperature anomalies which may indicate upcoming failures.
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More information
Published date: 7 June 2015
Venue - Dates:
2015 IEEE Electrical Insulation Conference (EIC), Seattle, United States, 2015-06-07 - 2015-06-10
Keywords:
condition monitoring, support vector regression, diagnostic, prognostic, insulation system
Organisations:
EEE
Identifiers
Local EPrints ID: 378111
URI: http://eprints.soton.ac.uk/id/eprint/378111
ISBN: 978-1-4799-7354-5
PURE UUID: 1805f191-dc47-41cc-b75f-c8a335b4c727
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Date deposited: 16 Jun 2015 15:47
Last modified: 15 Mar 2024 03:25
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Contributors
Author:
S. Christou
Author:
P.L. Lewin
Editor:
J.A. Pilgrim
Author:
S.G. Swingler
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