Cybersecurity Defence of Synchrophasors in Distribution Systems: A Deep Learning Approach
Paper
Paper/Presentation Title | Cybersecurity Defence of Synchrophasors in Distribution Systems: A Deep Learning Approach |
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Presentation Type | Paper |
Authors | Zhang, Ge, Cui, Yi, Zhang, Ruiyuan and Bai, Feifei |
Journal or Proceedings Title | Proceedings of 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG) |
Number of Pages | 6 |
Year | 2023 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | Australia |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ETFG55873.2023.10407305 |
Web Address (URL) of Paper | https://ieeexplore-ieee-org.ezproxy.usq.edu.au/document/10407305 |
Web Address (URL) of Conference Proceedings | https://ieeexplore-ieee-org.ezproxy.usq.edu.au/xpl/conhome/10406302/proceeding |
Conference/Event | 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG) |
Event Details | 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG) Delivery In person Event Date 03 to end of 06 Dec 2023 Event Location Wollongong, Australia |
Abstract | Phasor Measurement Unit (PMU) has become a critical component for the modern distribution network, as it records high-resolution synchrophasor data which contain abundant static and dynamic information of the system. However, PMUs are vulnerable to potential cyberattacks, for example, data spoofing attacks. A deliberate PMU spoofing attack can confuse the existing data source authentication models, especially when the models are used for identifying multiple PMUs at the same time. This paper proposes a data-driven cybersecurity defence model which can identify the source information of a large group of PMUs with high accuracy. The model utilizes the inherent correlations among PMUs with a deep neural network to enhance the data source authentication performance. The effectiveness of the proposed model is examined by the PMU data collected from a real distribution network with different error metrics. Through comprehensive numerical experiments, the proposed model provides consistent superior performance in comparison with other state-of-the-art data source identification approaches. |
Keywords | Source identification; deep learning; cybersecurity; distribution system; PMU |
ANZSRC Field of Research 2020 | 400803. Electrical energy generation (incl. renewables, excl. photovoltaics) |
460308. Pattern recognition | |
460403. Data security and protection | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Queensland |
School of Engineering | |
Hong Kong University of Science and Technology, China | |
Griffith University |
https://research.usq.edu.au/item/z4z15/cybersecurity-defence-of-synchrophasors-in-distribution-systems-a-deep-learning-approach
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