NDLI: Knowledge-based diagnosis of partial discharges in power transformers

Content Provider	IEEE Xplore Digital Library
Author	Strachan, S.M. Rudd, S. McArthur, S.D.J. Judd, M.D. Meijer, S. Gulski, E.
Copyright Year	1994
Abstract	The abstraction of meaningful diagnostic information from raw condition monitoring data in domains where diagnostic expertise and knowledge is limited and constantly evolving presents a significant research challenge. Expert diagnosis and location of partial discharges in high voltage electrical plant is one such domain. This paper describes the functionality of a knowledge-based decision support system capable of providing engineers with a comprehensive diagnosis of the defects responsible for partial discharge activity detected in oil-filled power transformers. Plant data captured from partial discharge (PD) sensors can be processed to generate phase-resolved partial discharge (PRPD) patterns. This paper proposes a means of abstracting the salient features characterizing the observed PRPD patterns. Captured knowledge describing the visual interpretation of these patterns can be applied for defect diagnosis and location. The knowledge-based PRPD pattern interpretation system can support on-line plant condition assessment and defect diagnosis by presenting a comprehensive diagnosis of PD activity detected and classification of the defect source. The paper also discusses how the system justifies its diagnosis of the PD activity to offer the expert greater confidence in the result, a feature generally absent in 'black-box' pattern recognition techniques. The incremental approach exhibited by the system reflects that of a PD expert's visual interpretation of the PRPD pattern. The paper describes how this functional system design has evolved from the approach taken by PD experts to the visual interpretation of PRPD patterns.
Sponsorship	National Natural Science Foundation of China (NSFC) Region Centre Ville de Tours Conseil General D'Indre et Loire ABB IEEE Sandia National Laboratories TOSHIBA
File Size	593534
File Format	PDF
ISSN	10709878
Volume Number	15
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Partial discharges Power transformers Condition monitoring Sensor phenomena and characterization Knowledge based systems Maintenance Power system reliability Power systems Decision support systems Knowledge engineering
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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Incremental knowledge-based partial discharge diagnosis in oil-filled power transformers

A generic knowledge-based approach to the analysis of partial discharge data

The design of a multi-agent transformer condition monitoring system

Review of condition assessment of power transformers in service

Optimisation of a sensor for onsite detection of partial discharges in power transformers by the UHF method

Overhead Line Preventive Maintenance Strategy Based on Condition Monitoring and System Reliability Assessment

An evidential reasoning approach to transformer condition assessments

The practical implications of bringing a multi-agent transformer condition monitoring system on-line

Managing Remote Online Partial Discharge Data

Knowledge-based diagnosis of partial discharges in power transformers

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Review of condition assessment of power transformers in service

Optimisation of a sensor for onsite detection of partial discharges in power transformers by the UHF method

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