Cited by 332Smart Grid Security and ResiliencePower System Optimization and StabilityElectricity Theft Detection Techniques

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Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach

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Zhang, Ying, Wang, Jianhui, Chen, Bo·2021·IEEE Transactions on Smart Grid

DOI 10.1109/tsg.2020.3010510

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Abstract

The dependence on advanced information and communication technology increases the vulnerability in smart grids under cyber-attacks. Recent research on unobservable false data injection attacks (FDIAs) reveals the high risk of secure system operation, since these attacks can bypass current bad data detection mechanisms. To mitigate this risk, this paper proposes a data-driven learning-based algorithm for detecting unobservable FDIAs in distribution systems. We use autoencoders for efficient dimension reduction and feature extraction of measurement datasets. Further, we integrate the autoencoders into an advanced generative adversarial network (GAN) framework, which successfully detects anomalies under FDIAs by capturing the unconformity between abnormal and secure measurements. Also, considering that the datasets collected from practical power systems are partially labeled due to expensive labeling costs and missing labels, the proposed method only requires a few labeled measurement data in addition to unlabeled data for training. Numerical simulations in three-phase unbalanced IEEE 13-bus and 123-bus distribution systems validate the detection accuracy and efficiency of this method.

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Show all 35 references

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Authors: Zhang, Y., Wang, J., & Chen, B.
Affiliations: Southern Methodist University (US)
Argonne National Laboratory (US)
ORCID: Zhang, Ying
Wang, Jianhui
Chen, Bo
Venue: IEEE Transactions on Smart Grid
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2021-01
Volume: 12
Issue: 1
Pages: 623-634
ISSN: 1949-3053 (ISSN-L)
1949-3061
DOI: 10.1109/tsg.2020.3010510
OpenAlex: W3044239919
Cited by: 332
References: 35
Citations by year: 2026: 40 · 2025: 83 · 2024: 66 · 2023: 61 · 2022: 58 · 2021: 20
FWCI: 17.68
Type: journal article
Language: en
Open access: green
www.osti.gov
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Smart Grid (publishedVersion)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Topics: Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
Physical Sciences › Engineering › Electrical and Electronic Engineering › Electricity Theft Detection Techniques
Keywords: false data injection attacks, smart grid security, bad data detection, distribution systems, autoencoder, generative adversarial networks, anomaly detection, semi-supervised learning, measurement data, cyberattacks

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Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach

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