Graph Embedding Dynamic Feature-based Supervised Contrastive Learning of Transient Stability for Changing Power Grid Topologies Article Swipe

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YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2308.00537

Accurate online transient stability prediction is critical for ensuring power system stability when facing disturbances. While traditional transient stablity analysis replies on the time domain simulations can not be quickly adapted to the power grid toplogy change. In order to vectorize high-dimensional power grid topological structure information into low-dimensional node-based graph embedding streaming data, graph embedding dynamic feature (GEDF) has been proposed. The transient stability GEDF-based supervised contrastive learning (GEDF-SCL) model uses supervised contrastive learning to predict transient stability with GEDFs, considering power grid topology information. To evaluate the performance of the proposed GEDF-SCL model, power grids of varying topologies were generated based on the IEEE 39-bus system model. Transient operational data was obtained by simulating N-1 and N-$\bm{m}$-1 contingencies on these generated power system topologies. Test result demonstrated that the GEDF-SCL model can achieve high accuracy in transient stability prediction and adapt well to changing power grid topologies.

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Network topology Transient (computer programming) Computer science Grid Topology (electrical circuits) Stability (learning theory) Embedding Electric power system Graph Power (physics) Artificial intelligence Machine learning Theoretical computer science Engineering Mathematics Physics Electrical engineering Geometry Quantum mechanics Operating system

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2308.00537
PDF: https://arxiv.org/pdf/2308.00537
OA Status: green
Cited By: 1
Related Works: 10
OpenAlex ID: https://openalex.org/W4385966126

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DOI: https://doi.org/10.48550/arxiv.2308.00537

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Title: Graph Embedding Dynamic Feature-based Supervised Contrastive Learning of Transient Stability for Changing Power Grid Topologies

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-08-01

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Authors: Zijian Lv, Xin Chen, Zijian Feng

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Landing page: https://arxiv.org/abs/2308.00537

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PDF URL: https://arxiv.org/pdf/2308.00537

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Concepts: Network topology, Transient (computer programming), Computer science, Grid, Topology (electrical circuits), Stability (learning theory), Embedding, Electric power system, Graph, Power (physics), Artificial intelligence, Machine learning, Theoretical computer science, Engineering, Mathematics, Physics, Electrical engineering, Geometry, Quantum mechanics, Operating system

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Cited by: 1

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