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Leak Detection for Self-Contained Fluid-Filled Cables using Regression Analysis

Record type: Conference or Workshop Item (Paper)

This paper investigates the methodology of the machine learning technique, namely the Support Vector Machine to assess the condition of fluid-filled high voltage cables based on thermal, pressure and load current information. Field data from a healthy circuit containing pressure, temperature and load current information have been obtained. The data structure has been investigated and a feasible algorithm to restructure the data for further analysis is proposed. The post-processing technique using Support Vector Machine Regression to predict oil pressure in the system is demonstrated. Results obtained using the regression analysis in this paper are very promising. Based on this method, an expert system could give early warning with better sensitivity than the existing system for the cable circuit and implementation of this approach can be achieved without taking the circuit out of service.

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Citation

Hao, L, Lewin, P L, Swingler, S G and Bradley, C (2010) Leak Detection for Self-Contained Fluid-Filled Cables using Regression Analysis At IEEE 2010 International Symposium on Electrical Insulation, United States. 06 - 09 Jun 2010. , CD-ROM.

More information

Published date: 6 June 2010
Additional Information: Event Dates: 6 - 9 June 2010
Venue - Dates: IEEE 2010 International Symposium on Electrical Insulation, United States, 2010-06-06 - 2010-06-09
Organisations: Electronics & Computer Science, EEE

Identifiers

Local EPrints ID: 271224
URI: http://eprints.soton.ac.uk/id/eprint/271224
ISBN: 978-1-4244-6300-8
PURE UUID: 532aec5f-1b55-46bc-ac9d-ccc67ced64d4

Catalogue record

Date deposited: 06 Jun 2010 19:50
Last modified: 18 Jul 2017 06:45

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Contributors

Author: L Hao
Author: P L Lewin
Author: S G Swingler
Author: C Bradley

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