Leveraging Sensory Data in Estimating Transformer Lifetime

TL;DR

This paper presents a method for estimating transformer lifetime by using sensory temperature data and a CMA model to provide hourly loss of life estimates, demonstrating effectiveness and practicality.

Contribution

It introduces a novel approach combining sensory temperature measurements with CMA modeling for transformer lifetime estimation, enhancing accuracy and real-time assessment.

Findings

Effective lifetime estimation demonstrated through numerical examples

CMA model provides convergent hourly loss of life estimates

Approach improves practicality and efficiency in transformer maintenance

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.