Data Requisites for Transformer Statistical Lifetime Modelling—Part I: Aging-Related Failures

Statistical lifetime models are regarded as an important part of the replacement management of power transformers. The development of transformer lifetime models, however, is hindered by the lack of failure data since most of the transformer fleets have not yet completed their first lifecycle. As researchers realized the importance of survival data, lots of lifetime models are developed based on failure data together with survival data. This paper analyzes the effect of survival data on the accuracy of lifetime models through a series of Monte Carlo simulations. It has been proved that the accuracy of lifetime models can be improved by taking the survival data into account. However, the degree of improvement is greatly confined by the censoring rate and the sample size of the collected lifetime data. Practical implications of the simulation results and suggestions on measures to further improve the accuracy of lifetime models are subsequently provided.

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