The industrialisation of a multi-agent system for power transformer condition monitoring

V.M. Catterson, S.D.J. McArthur

Research output: Contribution to conferencePaper

Abstract

Electrical utilities have a pressing need for help with asset management, particularly for large plant items such as transformers. Transformer aging, problems and faults are intimated by partial discharge activity, which can be categorised into defect types. This can be achieved by a condition monitoring system using multiple intelligent classification techniques to provide accurate diagnoses. On-line operation of this system will remove the data processing burden from personnel, allowing for concentration on alleviating the fault's effects rather than interpreting raw data.

Conference

Conference24th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI-2004)
CountryUnited Kingdom
CityCambridge
Period12/12/0415/12/04

Fingerprint

Asset management
Power transformers
Partial discharges
Condition monitoring
Multi agent systems
Aging of materials
Personnel
Defects

Keywords

  • industrialisation
  • multi-agent system
  • power transformer
  • condition monitoring

Cite this

Catterson, V. M., & McArthur, S. D. J. (2004). The industrialisation of a multi-agent system for power transformer condition monitoring. 165-178. Paper presented at 24th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI-2004) , Cambridge, United Kingdom. https://doi.org/10.1007/1-84628-103-2_12
Catterson, V.M. ; McArthur, S.D.J. / The industrialisation of a multi-agent system for power transformer condition monitoring. Paper presented at 24th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI-2004) , Cambridge, United Kingdom.13 p.
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Catterson, VM & McArthur, SDJ 2004, 'The industrialisation of a multi-agent system for power transformer condition monitoring' Paper presented at 24th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI-2004) , Cambridge, United Kingdom, 12/12/04 - 15/12/04, pp. 165-178. https://doi.org/10.1007/1-84628-103-2_12

The industrialisation of a multi-agent system for power transformer condition monitoring. / Catterson, V.M.; McArthur, S.D.J.

2004. 165-178 Paper presented at 24th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI-2004) , Cambridge, United Kingdom.

Research output: Contribution to conferencePaper

TY - CONF

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AU - McArthur, S.D.J.

PY - 2004

Y1 - 2004

N2 - Electrical utilities have a pressing need for help with asset management, particularly for large plant items such as transformers. Transformer aging, problems and faults are intimated by partial discharge activity, which can be categorised into defect types. This can be achieved by a condition monitoring system using multiple intelligent classification techniques to provide accurate diagnoses. On-line operation of this system will remove the data processing burden from personnel, allowing for concentration on alleviating the fault's effects rather than interpreting raw data.

AB - Electrical utilities have a pressing need for help with asset management, particularly for large plant items such as transformers. Transformer aging, problems and faults are intimated by partial discharge activity, which can be categorised into defect types. This can be achieved by a condition monitoring system using multiple intelligent classification techniques to provide accurate diagnoses. On-line operation of this system will remove the data processing burden from personnel, allowing for concentration on alleviating the fault's effects rather than interpreting raw data.

KW - industrialisation

KW - multi-agent system

KW - power transformer

KW - condition monitoring

U2 - 10.1007/1-84628-103-2_12

DO - 10.1007/1-84628-103-2_12

M3 - Paper

SP - 165

EP - 178

ER -

Catterson VM, McArthur SDJ. The industrialisation of a multi-agent system for power transformer condition monitoring. 2004. Paper presented at 24th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI-2004) , Cambridge, United Kingdom. https://doi.org/10.1007/1-84628-103-2_12