Picture of neon light reading 'Open'

Discover open research at Strathprints as part of International Open Access Week!

23-29 October 2017 is International Open Access Week. The Strathprints institutional repository is a digital archive of Open Access research outputs, all produced by University of Strathclyde researchers.

Explore recent world leading Open Access research content this Open Access Week from across Strathclyde's many research active faculties: Engineering, Science, Humanities, Arts & Social Sciences and Strathclyde Business School.

Explore all Strathclyde Open Access research outputs...

Prognostic modeling of transformer aging using Bayesian particle filtering

Catterson, Victoria (2014) Prognostic modeling of transformer aging using Bayesian particle filtering. In: 2014 IEEE Conference on Electrical Insulation and Dielectric Phenomena, 2014-10-19 - 2014-10-22.

[img] PDF (Catterson-CEIDP2014-prognostic-modeling-of-transformer-aging)
Catterson_CEIDP2014_prognostic_modeling_of_transformer_aging.pdf - Accepted Author Manuscript

Download (321kB)

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.