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Electrical Engineering
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15-06-2024 | Original Paper

An unsupervised false data detection method based on graph autoencoder and attention network in power grid

Authors: Yingjie Yang, Tiantian Cai, Dehong Liu, Xueping Li, Yaokun Wang, Zhigang Lu

Published in: Electrical Engineering | Issue 1/2025

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Abstract

The article introduces an unsupervised deep learning method for detecting false data injection attacks (FDIAs) in power grids using graph autoencoder and attention network (GAE–GAT). The method addresses the challenges posed by FDIAs, which can bypass conventional state estimation methods and affect the stable operation of power grids. By leveraging the GAE–GAT model, the proposed method captures both power grid topology and operational data, achieving high detection accuracy and robustness against topology changes. The article highlights the advantages of the GAE–GAT model in handling complex interactions between attribute data and topology information, making it a promising solution for FDIA detection in power systems.
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Metadata
Title
An unsupervised false data detection method based on graph autoencoder and attention network in power grid
Authors
Yingjie Yang
Tiantian Cai
Dehong Liu
Xueping Li
Yaokun Wang
Zhigang Lu
Publication date
15-06-2024
Publisher
Springer Berlin Heidelberg
Published in
Electrical Engineering / Issue 1/2025
Print ISSN: 0948-7921
Electronic ISSN: 1432-0487
DOI
https://doi.org/10.1007/s00202-024-02520-7

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