Title: Leveraging Sensory Data in Estimating Transformer Lifetime,
Abstract: Transformer lifetime assessments plays a vital role in reliable operation of
power systems. In this paper, leveraging sensory data, an approach in
estimating transformer lifetime is presented. The winding hottest-spot
temperature, which is the pivotal driver that impacts transformer aging, is
measured hourly via a temperature sensor, then transformer loss of life is
calculated based on the IEEE Std. C57.91-2011. A Cumulative Moving Average
(CMA) model is subsequently applied to the data stream of the transformer loss
of life to provide hourly estimates until convergence. Numerical examples
demonstrate the effectiveness of the proposed approach for the transformer
lifetime estimation, and explores its efficiency and practical merits.