Dynamic Transformer Rating (DTR) allows optimizing the transformer performance under time-varying load and/or environmental conditions. Traditionally, the DTR is applied controlling only the winding Hottest-Spot Temperature (HST). However, the most recent literature evidences that the DTR should simultaneously consider limitations on the current, HST and Top-Oil Temperature (TOT) in the tank. Due to the uncertainties involved in the DTR estimation, the problem should be framed within a probabilistic approach. In this paper, a novel comprehensive probabilistic approach is proposed (i) to predict the probability that TOT, HST and current are below their assigned limits, and (ii) to provide, through a naïve alarm-setting strategy, a warning if the probability is lower than a threshold. Numerical applications on actual data give evidence of the suitability of the proposal and give useful information to understand when, if and how each limit constrains the problem.

A Probabilistic Approach for Dynamic Oil-Immersed Transformer Rating Accounting for Current and Temperature Limits / Bracale, A.; Caramia, P.; Carpinelli, G.; De Falco, P.; Russo, A.. - (2022), pp. 1-6. (Intervento presentato al convegno 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022 nel 2022) [10.1109/PMAPS53380.2022.9810611].

A Probabilistic Approach for Dynamic Oil-Immersed Transformer Rating Accounting for Current and Temperature Limits

Russo A.
2022

Abstract

Dynamic Transformer Rating (DTR) allows optimizing the transformer performance under time-varying load and/or environmental conditions. Traditionally, the DTR is applied controlling only the winding Hottest-Spot Temperature (HST). However, the most recent literature evidences that the DTR should simultaneously consider limitations on the current, HST and Top-Oil Temperature (TOT) in the tank. Due to the uncertainties involved in the DTR estimation, the problem should be framed within a probabilistic approach. In this paper, a novel comprehensive probabilistic approach is proposed (i) to predict the probability that TOT, HST and current are below their assigned limits, and (ii) to provide, through a naïve alarm-setting strategy, a warning if the probability is lower than a threshold. Numerical applications on actual data give evidence of the suitability of the proposal and give useful information to understand when, if and how each limit constrains the problem.
2022
978-1-6654-1211-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2971740