SmarTrafo: A Probabilistic Predictive Tool for Dynamic Transformer Rating

Operating electrical networks by Dynamic Transformer Rating (DTR) unlocks the capacity of grids and allows to increase the exploitation of distributed generation and renewables. However, there is risk associated with the operation of transformers by DTR. Energy dispatch and/or load curtailment must...

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Bibliographic Details
Published in:IEEE transactions on power delivery 2021-06, Vol.36 (3), p.1619-1630
Main Authors: Bracale, Antonio, Caramia, Pierluigi, Carpinelli, Guido, De Falco, Pasquale
Format: Article
Language:English
Subjects:
Distributed generation
Dynamic transformer rating
Electrical networks
Load modeling
Multiple objective analysis
Oil insulation
Power system dynamics
Power transformer insulation
Probabilistic logic
quantile regression forests
Random variables
reliability assessment
Statistical analysis
Strategy
stress-strength models
Transformers
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Summary:Operating electrical networks by Dynamic Transformer Rating (DTR) unlocks the capacity of grids and allows to increase the exploitation of distributed generation and renewables. However, there is risk associated with the operation of transformers by DTR. Energy dispatch and/or load curtailment must be scheduled before the actual energy delivery, thus DTR and load should be predicted prior the actual transformer loading. Since both are random variables, this problem is prone to be addressed by stress-strength analysis. In this paper, the novel comprehensive probabilistic tool "SmarTrafo" is presented. It allows predicting the probability of the DTR to be greater than the load (i.e., the probability of success) through an exact analytic stress-strength model, and formulating an alarm-setting strategy in order to establish a warning if the expected probability is greater than a threshold. Specifically, the threshold is optimized in a multi-objective formulation, exploiting three different indices which differently evaluate the predictive skill of alarm-setting strategy. Numerical experiments based on actual data confirm the suitability of the proposal in predicting the probability of success and in establishing high-performance alarms based on such predictions.
ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2020.3012180