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On Zero-shot Learning in Neural State Estimation of Power Distribution Systems

Authors:
Aleksandr Berezin
Stephan Balduin
Eric MSP Veith
Thomas Oberließen
Sebastian Peter

Keywords: neural state estimation, zero-shot learning, transfer learning, graph neural networks

Abstract:
This paper addresses the challenge of neural state estimation in power distribution systems. We identified a research gap in the current state of the art, which lies in the inability of models to adapt to changes in the power grid, such as loss of sensors and branch switching, in a zero-shot fashion. Based on the literature, we identified graph neural networks as the most promising class of models for this use case. Our experiments confirm their robustness to some grid changes and also show that a deeper network does not always perform better. We propose data augmentations to improve performance and conduct a comprehensive grid search of different model configurations for common zero-shot learning scenarios.

Pages: 47 to 52

Copyright: Copyright (c) IARIA, 2025

Publication date: March 9, 2025

Published in: conference

ISSN: 2308-412X

ISBN: 978-1-68558-242-5

Location: Lisbon, Portugal

Dates: from March 9, 2025 to March 13, 2025