An unsupervised false data detection method based on graph autoencoder and attention network in power grid
Author:
Funder
National Natural Science Foundation of China
Natural Science Foundation of Hebei Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00202-024-02520-7.pdf
Reference54 articles.
1. Li J, Sun C, Su Q (2021) Analysis of cascading failures of power cyber-physical systems considering false data injection attacks. Glob Energy Interconnect 4(2):204–213
2. Liu Y, Ning P, Reiter MK (2011) False data injection attacks against state estimation in electric power grids. ACM Trans Inf Syst Secur 14(1):1–33
3. Shefaei A, Mohammadpourfard M, Mohammadi-ivatloo B, Weng Y (2022) Revealing a new vulnerability of distributed state estimation: a data integrity attack and an unsupervised detection algorithm. IEEE Trans Control Netw Syst 9(2):706–718
4. Ruan J, Wang H, Liu Y, Aziz S, Peng J, Wang G (2017) AC sparse modeling for false data injection attack on smart gird. In: 2017 Asian Conference on energy, power and transportation electrification (ACEPT), Singapore, pp 1–5
5. Zhao J, Zhang G, Dong Z, Wong K (2016) Forecasting-aided imperfect false data injection attacks against power system nonlinear state estimation. IEEE Trans Smart Grid 7(1):6–8
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