Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems

Li, Qi; Zhang, Jinan; Zhao, Junbo; Ye, Jin; Song, Wenzhan; Li, Fangyu

doi:10.1109/tsg.2022.3148233

Title: Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems

Journal Article · 31 January 2022 · IEEE Transactions on Smart Grid

DOI: https://doi.org/10.1109/tsg.2022.3148233 · OSTI ID:1980586

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University of Georgia, Athens, GA (United States); University of Georgia
University of Georgia, Athens, GA (United States)
Mississippi State University, Mississippi State, MS (United States)
Beijing University of Technology (China)

Development of a cyber security strategy for the active distribution systems is challenging due to the inclusion of distributed renewable energy generations. Here this paper proposes an adaptive hierarchical cyber attack detection and localization framework for distributed active distribution systems via analyzing electrical waveforms. Cyber attack detection is based on a sequential deep learning model, via which even minor cyber attacks can be identified. The two-stage cyber attack localization algorithm first estimates the cyber attack sub-region, and then localize the specified cyber attack within the estimated subregion. We propose a modified spectral clustering-based network partitioning method for the hierarchical cyber attack ‘coarse’ localization. Next, to further narrow down the cyber attack location, a normalized impact score based on waveform statistical metrics is proposed to obtain a ‘fine’ cyber attack location by characterizing different waveform properties. Finally, compared with classical and state-of-art methods, a comprehensive quantitative evaluation with two case studies shows promising estimation results of the proposed framework.

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Research Organization:: University of Arkansas, Fayetteville, AR (United States); University of Georgia, Athens, GA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: EE0009026

OSTI ID:: 1980586

Journal Information:: IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 3 Vol. 13; ISSN 1949-3053

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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