Artificial neural networks as a tool for partial discharge source recognition

J. R. Hyde; I. J. Kemp; A. Nesbitt

Artificial neural networks as a tool for partial discharge source recognition

J. R. Hyde^*
, I. J. Kemp
, A. Nesbitt

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

Abstract

Partial discharge (PD) activity is both a symptom of degradation in the solid insulating systems of power plant - irrespective of the causative stresses - and a stress mechanism which causes degradation. It can therefore be readily appreciated why the detection, location and interpretation of partial discharge activity, towards a characterisation of the nature and form of degradation, is an attractive technique to the diagnostic engineer.

Original language	English
Pages	93-96
Number of pages	4
Publication status	Published - 1995
Event	UPEC '95 - 30th Universities Power Engineering Conference - London, United Kingdom Duration: 5 Sept 1995 → 7 Sept 1995

Conference

Conference	UPEC '95 - 30th Universities Power Engineering Conference
Abbreviated title	UPEC '95
Country/Territory	United Kingdom
City	London
Period	5/09/95 → 7/09/95

ASJC Scopus subject areas

General Engineering
General Energy

Cite this

@conference{a857a82180ca4f3f8f5fe9edc8b2ec6c,

title = "Artificial neural networks as a tool for partial discharge source recognition",

abstract = "Partial discharge (PD) activity is both a symptom of degradation in the solid insulating systems of power plant - irrespective of the causative stresses - and a stress mechanism which causes degradation. It can therefore be readily appreciated why the detection, location and interpretation of partial discharge activity, towards a characterisation of the nature and form of degradation, is an attractive technique to the diagnostic engineer.",

author = "Hyde, \{J. R.\} and Kemp, \{I. J.\} and A. Nesbitt",

year = "1995",

language = "English",

pages = "93--96",

note = "UPEC '95 - 30th Universities Power Engineering Conference, UPEC '95 ; Conference date: 05-09-1995 Through 07-09-1995",

}

TY - CONF

T1 - Artificial neural networks as a tool for partial discharge source recognition

AU - Hyde, J. R.

AU - Kemp, I. J.

AU - Nesbitt, A.

PY - 1995

Y1 - 1995

N2 - Partial discharge (PD) activity is both a symptom of degradation in the solid insulating systems of power plant - irrespective of the causative stresses - and a stress mechanism which causes degradation. It can therefore be readily appreciated why the detection, location and interpretation of partial discharge activity, towards a characterisation of the nature and form of degradation, is an attractive technique to the diagnostic engineer.

AB - Partial discharge (PD) activity is both a symptom of degradation in the solid insulating systems of power plant - irrespective of the causative stresses - and a stress mechanism which causes degradation. It can therefore be readily appreciated why the detection, location and interpretation of partial discharge activity, towards a characterisation of the nature and form of degradation, is an attractive technique to the diagnostic engineer.

UR - https://www.scopus.com/pages/publications/0029502835

M3 - Paper

AN - SCOPUS:0029502835

SP - 93

EP - 96

T2 - UPEC '95 - 30th Universities Power Engineering Conference

Y2 - 5 September 1995 through 7 September 1995

ER -

Artificial neural networks as a tool for partial discharge source recognition

Abstract

Conference

ASJC Scopus subject areas

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