Prognostic modeling of transformer aging using Bayesian particle filtering

Victoria Catterson

Prognostic modeling of transformer aging using Bayesian particle filtering

Victoria Catterson

Electronic And Electrical Engineering

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

6 Citations (Scopus)

318 Downloads (Pure)

Abstract

The goal of condition monitoring is to accurately assess the current health of an asset, in order to generate a prognosis, i.e. predict its remaining useful life. In the absence of a fault which causes premature failure, transformer degradation is linked to paper aging. Research and experience have resulted in models of paper aging where hotspot temperature is the key driver. However, these deterministic equations give a false sense of certainty about remaining insulation life.

This paper demonstrates the use of Bayesian particle filtering for transformer life prognostics. This technique allows quantification of the uncertainties surrounding aspects such as the initial degree of polymerization of the paper, the relationship between hotspot temperature and measurands, and the accuracy of measurements. A case study from an in-service 180 MVA transformer is used to illustrate its potential.

Original language	English
Publication status	Published - Oct 2014
Event	2014 IEEE Conference on Electrical Insulation and Dielectric Phenomena - Des Moines, United States Duration: 19 Oct 2014 → 22 Oct 2014

Conference

Conference	2014 IEEE Conference on Electrical Insulation and Dielectric Phenomena
Country/Territory	United States
City	Des Moines
Period	19/10/14 → 22/10/14

Keywords

remaining useful life
RUL
Bayesian particle filtering
transformer life
transformer defect diagnosis
transformer degradation

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Catterson-CEIDP2014-prognostic-modeling-of-transformer-agingAccepted author manuscript, 314 KB

Cite this

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title = "Prognostic modeling of transformer aging using Bayesian particle filtering",

abstract = "The goal of condition monitoring is to accurately assess the current health of an asset, in order to generate a prognosis, i.e. predict its remaining useful life. In the absence of a fault which causes premature failure, transformer degradation is linked to paper aging. Research and experience have resulted in models of paper aging where hotspot temperature is the key driver. However, these deterministic equations give a false sense of certainty about remaining insulation life.This paper demonstrates the use of Bayesian particle filtering for transformer life prognostics. This technique allows quantification of the uncertainties surrounding aspects such as the initial degree of polymerization of the paper, the relationship between hotspot temperature and measurands, and the accuracy of measurements. A case study from an in-service 180 MVA transformer is used to illustrate its potential.",

keywords = "remaining useful life, RUL, Bayesian particle filtering, transformer life, transformer defect diagnosis, transformer degradation",

author = "Victoria Catterson",

note = "(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.; 2014 IEEE Conference on Electrical Insulation and Dielectric Phenomena ; Conference date: 19-10-2014 Through 22-10-2014",

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N2 - The goal of condition monitoring is to accurately assess the current health of an asset, in order to generate a prognosis, i.e. predict its remaining useful life. In the absence of a fault which causes premature failure, transformer degradation is linked to paper aging. Research and experience have resulted in models of paper aging where hotspot temperature is the key driver. However, these deterministic equations give a false sense of certainty about remaining insulation life.This paper demonstrates the use of Bayesian particle filtering for transformer life prognostics. This technique allows quantification of the uncertainties surrounding aspects such as the initial degree of polymerization of the paper, the relationship between hotspot temperature and measurands, and the accuracy of measurements. A case study from an in-service 180 MVA transformer is used to illustrate its potential.

AB - The goal of condition monitoring is to accurately assess the current health of an asset, in order to generate a prognosis, i.e. predict its remaining useful life. In the absence of a fault which causes premature failure, transformer degradation is linked to paper aging. Research and experience have resulted in models of paper aging where hotspot temperature is the key driver. However, these deterministic equations give a false sense of certainty about remaining insulation life.This paper demonstrates the use of Bayesian particle filtering for transformer life prognostics. This technique allows quantification of the uncertainties surrounding aspects such as the initial degree of polymerization of the paper, the relationship between hotspot temperature and measurands, and the accuracy of measurements. A case study from an in-service 180 MVA transformer is used to illustrate its potential.

KW - remaining useful life

KW - RUL

KW - Bayesian particle filtering

KW - transformer life

KW - transformer defect diagnosis

KW - transformer degradation

UR - http://sites.ieee.org/ceidp-2014/

M3 - Paper

T2 - 2014 IEEE Conference on Electrical Insulation and Dielectric Phenomena

Y2 - 19 October 2014 through 22 October 2014

ER -

Prognostic modeling of transformer aging using Bayesian particle filtering

Abstract

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Keywords

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