Leveraging Sensory Data in Estimating Transformer Lifetime
For power system operators, this provides a practical method for real-time transformer lifetime estimation using sensory data, though it is an incremental application of existing standards and models.
This paper presents an approach for estimating transformer lifetime using hourly sensory data on winding hottest-spot temperature, applying the IEEE Std. C57.91-2011 for loss-of-life calculation and a Cumulative Moving Average model for hourly estimates. Numerical examples demonstrate the effectiveness and practical merits of the approach.
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.