On zero-shot learning in neural state estimation of power distribution systems

• URL: http://arxiv.org/abs/2408.05787v2
• Date: Fri, 30 May 2025 18:41:19 GMT
• Title: On zero-shot learning in neural state estimation of power distribution systems
• Authors: Aleksandr Berezin, Stephan Balduin, Thomas Oberließen, Sebastian Peter, Eric MSP Veith,
• Abstract summary: This paper addresses the challenge of neural state estimation in power distribution systems.<n>We identify graph neural networks as the most promising class of models for this use case.<n>We propose data augmentations to improve performance and conduct a comprehensive grid search of different model configurations for common zero-shot learning scenarios.
• Score: 39.58317527488534
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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