Comparison of parameter estimation methods for transformer Weibull lifetime modelling

Abstract

Two-parameter Weibull distribution is the most widely adopted lifetime model for power transformers. An appropriate parameter estimation method is essential to guarantee the accuracy of a derived Weibull lifetime model. Six popular parameter estimation methods (i.e. the maximum likelihood estimation method, two median rank regression methods including the one regressing X on Y and the other one regressing Y on X, the Kaplan-Meier method, the method based on cumulative hazard plot, and the Li's method) are reviewed and compared in order to find the optimal one that suits transformer's Weibull lifetime modelling. The comparison took several different scenarios into consideration: 10000 sets of lifetime data, each of which had a sampling size of 40 1000 and a censoring rate of 90\%, were obtained by Monte-Carlo simulations for each scienario. Scale and shape parameters of Weibull distribution estimated by the six methods, as well as their mean value, median value and 90\% confidence band are obtained. The cross comparison of these results reveals that, among the six methods, the maximum likelihood method is the best one, since it could provide the most accurate Weibull parameters, i.e. parameters having the smallest bias in both mean and median values, as well as the shortest length of the 90\% confidence band. The maximum likelihood method is therefore recommended to be used over the other methods in transformer Weibull lifetime modelling.