Abstract
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 | United States |
| City | Des Moines |
| Period | 19/10/14 → 22/10/14 |
Fingerprint
Keywords
- remaining useful life
- RUL
- Bayesian particle filtering
- transformer life
- transformer defect diagnosis
- transformer degradation
Cite this
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Prognostic modeling of transformer aging using Bayesian particle filtering. / Catterson, Victoria.
2014. Paper presented at 2014 IEEE Conference on Electrical Insulation and Dielectric Phenomena, Des Moines, United States.Research output: Contribution to conference › Paper
TY - CONF
T1 - Prognostic modeling of transformer aging using Bayesian particle filtering
AU - Catterson, Victoria
N1 - (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.
PY - 2014/10
Y1 - 2014/10
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
ER -