Arrhenius modelling of paper insulation degradation was used to estimate population reliability and evaluate the expected replacement wave of power transformers aged 30 to 60 years in the Netherlands.
A statistical failure model based on Arrhenius modelling can estimate population reliability and evaluate replacement scenarios for aging power transformers.
The age of the majority of power transformers installed in the western electricity network reaches 30 to 60 years and replacement on short term seems eminent. A technically sound policy concerning the replacement of these assets requires a model that estimates the life expectancies of individual components and from that calculates parameters related to the behavior of a population of assets as a whole. We will illustrate the approach by applying it to a well-known degradation process: thermal degradation of the transformer paper insulation. In this paper we focus on the determination of the population reliability from individual reliabilities. These individual reliabilities are based on Arrhenius modelling of paper insulation degradation including its inherent uncertainties. A statistical failure model is used to obtain the population reliability figures. We demonstrate the modelling method by applying it to an existing population of power transformers in the Netherlands, to evaluate the expected replacement wave. Further, the model is applied to analyse alternative replacement scenarios.
Cahill et al. (Sat,) conducted a other in Power transformer degradation. Arrhenius modelling was evaluated on Population reliability. Arrhenius modelling of paper insulation degradation was used to estimate population reliability and evaluate the expected replacement wave of power transformers aged 30 to 60 years in the Netherlands.