Published Paper Details:

Prediction of the remaining life of high-voltage power transformers is an important issue for energy companies because of the need for planning maintenance and capital expenditures. Lifetime data for such transformers are complicated because transformer lifetimes can extend over many decades and transformer designs and manufacturing practices have evolved. In this system develop statistically-based predictions for the lifetimes of an energy company�s fleet of high-voltage transmission and distribution transformers. The company�s data records begin in 1980, providing information on the installation and failure dates of transformers. Although the dataset contains many units that were installed before 1980, there is no information about units that were installed and failed before 1980. Thus, the data are left truncated and right-censored. We use a parametric lifetime model to describe the lifetime distribution of individual transformers. We develop a statistical procedure, based on machine learning models, for computing a prediction interval for remaining life for individual transformers now in service. Then extend these ideas to provide predictions and prediction intervals for the cumulative number of failures, over a range of time, for the overall fleet of transformers. Using machine learning algorithms effectively got the power transformer loss values then it was sent through the mail to the server.