Compositional modelling of partial discharge pulse spectral characteristics

Peter Baker, Bruce Stephen, Martin Judd

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Partial discharge (PD) monitoring is an established method for insulation health monitoring in high voltage plant. A number of different approaches to PD defect diagnosis have been developed to extract defect-specific information from PD pulse data in both the time and frequency domains. Frequency based PD pulse analysis has previously been demonstrated to offer a low-power approach to PD defect identification, where a mixture of passive and active analog electronics can be used to generate diagnostic features in a low-power device suited to wireless sensor network operation. This paper examines approaches to implementing diagnostic methods for frequency-based PD pulse diagnosis targeted at compositional frequency spectrum features in a computationally efficient manner. Dirichlet and Gaussian distributions are used to demonstrate the complex probabilistic form of fault class decision surfaces, which motivates the proposed application of the log ratio transform to frequency composition data. The results demonstrate that PD defects can be differentiated using these frequency-based methods and that employing the log ratio transform to the compositional frequency content data yields increases in classification accuracy without necessarily resorting to more complex classifiers.
LanguageEnglish
Pages1909 - 1916
Number of pages8
JournalIEEE Transactions on Instrumentation and Measurement
Volume62
Issue number7
Early online date11 Mar 2013
DOIs
Publication statusPublished - 1 Jul 2013

Fingerprint

Partial discharges
pulses
Defects
defects
Monitoring
Gaussian distribution
classifiers
normal density functions
insulation
health
Insulation
Wireless sensor networks
high voltages
Classifiers
Electronic equipment
Health
analogs
sensors
Electric potential
Chemical analysis

Keywords

  • compositional modelling
  • spectral characteristics
  • partial discharge
  • pulse

Cite this

@article{ec45f6659e3e4e91bbd10dbbfae65ece,
title = "Compositional modelling of partial discharge pulse spectral characteristics",
abstract = "Partial discharge (PD) monitoring is an established method for insulation health monitoring in high voltage plant. A number of different approaches to PD defect diagnosis have been developed to extract defect-specific information from PD pulse data in both the time and frequency domains. Frequency based PD pulse analysis has previously been demonstrated to offer a low-power approach to PD defect identification, where a mixture of passive and active analog electronics can be used to generate diagnostic features in a low-power device suited to wireless sensor network operation. This paper examines approaches to implementing diagnostic methods for frequency-based PD pulse diagnosis targeted at compositional frequency spectrum features in a computationally efficient manner. Dirichlet and Gaussian distributions are used to demonstrate the complex probabilistic form of fault class decision surfaces, which motivates the proposed application of the log ratio transform to frequency composition data. The results demonstrate that PD defects can be differentiated using these frequency-based methods and that employing the log ratio transform to the compositional frequency content data yields increases in classification accuracy without necessarily resorting to more complex classifiers.",
keywords = "compositional modelling , spectral characteristics, partial discharge , pulse",
author = "Peter Baker and Bruce Stephen and Martin Judd",
year = "2013",
month = "7",
day = "1",
doi = "10.1109/TIM.2013.2247711",
language = "English",
volume = "62",
pages = "1909 -- 1916",
journal = "IEEE Transactions on Instrumentation and Measurement",
issn = "0018-9456",
number = "7",

}

Compositional modelling of partial discharge pulse spectral characteristics. / Baker, Peter; Stephen, Bruce; Judd, Martin.

In: IEEE Transactions on Instrumentation and Measurement, Vol. 62, No. 7, 01.07.2013, p. 1909 - 1916.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Compositional modelling of partial discharge pulse spectral characteristics

AU - Baker, Peter

AU - Stephen, Bruce

AU - Judd, Martin

PY - 2013/7/1

Y1 - 2013/7/1

N2 - Partial discharge (PD) monitoring is an established method for insulation health monitoring in high voltage plant. A number of different approaches to PD defect diagnosis have been developed to extract defect-specific information from PD pulse data in both the time and frequency domains. Frequency based PD pulse analysis has previously been demonstrated to offer a low-power approach to PD defect identification, where a mixture of passive and active analog electronics can be used to generate diagnostic features in a low-power device suited to wireless sensor network operation. This paper examines approaches to implementing diagnostic methods for frequency-based PD pulse diagnosis targeted at compositional frequency spectrum features in a computationally efficient manner. Dirichlet and Gaussian distributions are used to demonstrate the complex probabilistic form of fault class decision surfaces, which motivates the proposed application of the log ratio transform to frequency composition data. The results demonstrate that PD defects can be differentiated using these frequency-based methods and that employing the log ratio transform to the compositional frequency content data yields increases in classification accuracy without necessarily resorting to more complex classifiers.

AB - Partial discharge (PD) monitoring is an established method for insulation health monitoring in high voltage plant. A number of different approaches to PD defect diagnosis have been developed to extract defect-specific information from PD pulse data in both the time and frequency domains. Frequency based PD pulse analysis has previously been demonstrated to offer a low-power approach to PD defect identification, where a mixture of passive and active analog electronics can be used to generate diagnostic features in a low-power device suited to wireless sensor network operation. This paper examines approaches to implementing diagnostic methods for frequency-based PD pulse diagnosis targeted at compositional frequency spectrum features in a computationally efficient manner. Dirichlet and Gaussian distributions are used to demonstrate the complex probabilistic form of fault class decision surfaces, which motivates the proposed application of the log ratio transform to frequency composition data. The results demonstrate that PD defects can be differentiated using these frequency-based methods and that employing the log ratio transform to the compositional frequency content data yields increases in classification accuracy without necessarily resorting to more complex classifiers.

KW - compositional modelling

KW - spectral characteristics

KW - partial discharge

KW - pulse

UR - http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4407674

U2 - 10.1109/TIM.2013.2247711

DO - 10.1109/TIM.2013.2247711

M3 - Article

VL - 62

SP - 1909

EP - 1916

JO - IEEE Transactions on Instrumentation and Measurement

T2 - IEEE Transactions on Instrumentation and Measurement

JF - IEEE Transactions on Instrumentation and Measurement

SN - 0018-9456

IS - 7

ER -