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This content will become publicly available on June 4, 2024

Title: Physics-Informed Transfer Learning for Voltage Stability Margin Prediction
Assessing set-membership and evaluating distances to the related set boundary are problems of widespread interest, and can often be computationally challenging. Seeking efficient learning models for such tasks, this paper deals with voltage stability margin prediction for power systems. Supervised training of such models is conventionally hard due to high-dimensional feature space, and a cumbersome label-generation process. Nevertheless, one may find related easy auxiliary tasks, such as voltage stability verification, that can aid in training for the hard task. This paper develops a novel approach for such settings by leveraging transfer learning. A Gaussian process-based learning model is efficiently trained using learning- and physics-based auxiliary tasks. Numerical tests demonstrate markedly improved performance that is harnessed alongside the benefit of uncertainty quantification to suit the needs of the considered application.  more » « less
Award ID(s):
2220292 2212318 2126052 2128593
NSF-PAR ID:
10424913
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
International Conference on Data Mining
Page Range / eLocation ID:
1 to 5
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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