Data Source Authentication for Wide-Area Synchrophasor Measurements Based On Spatial Signature Extraction and Quadratic Kernel SVM
Article
Article Title | Data Source Authentication for Wide-Area Synchrophasor Measurements Based On Spatial Signature Extraction and Quadratic Kernel SVM |
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ERA Journal ID | 4478 |
Article Category | Article |
Authors | Liu, Shengyuan (Author), You, Shutang (Author), Yin, He (Author), Lin, Zhenzhi (Author), Liu, Yilu (Author), Cui, Yi (Author), Yao, Wenxuan (Author) and Sundaresh, Lakshmi (Author) |
Journal Title | International Journal of Electrical Power and Energy Systems |
Journal Citation | 140, pp. 1-13 |
Article Number | 108083 |
Number of Pages | 13 |
Year | 2022 |
Place of Publication | United Kingdom |
ISSN | 0142-0615 |
1879-3517 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ijepes.2022.108083 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0142061522001259 |
Abstract | As essential components of the wide-area measurement system (WAMS), phasor measurement units (PMUs), frequency disturbance recorders (FDRs) and universal grid analyzers (UGAs) collect valuable data continuously to reveal the dynamic variations of power systems and to enhance the operators’ situational awareness ability. However, these devices are vulnerable to multiple types of data exception emerging in recent years, such as data source ID mix exception spoofing, substantially threatening system security. To ensure the cyber security of WAMS, this work proposes a new spatial signature extraction method, followed by the quadratic kernel support vector machine (QKSVM)-based algorithm, to authenticate data source in WAMS. First, the load–frequency characteristic (LFC), which can represent the impacts of load variations on frequency, is utilized to extract the spatial signatures of FDRs located in different regions. Then, the quadratic kernel function is employed in the QKSVM-based algorithm to map the signatures into Hilbert space to authenticate the data source more accurately. Finally, case studies in the U.S. Western and Eastern power systems show that the proposed model-free algorithm is less sensitive to system sizes, and can achieve a higher authentication accuracy in a much shorter window length compared with other algorithms. |
Keywords | Data source authentication; quadratic kernel support vector machine (QKSVM); Spatial signature extraction; Synchrophasor measurement; wide-area measurement system (WAMS) |
ANZSRC Field of Research 2020 | 460403. Data security and protection |
400803. Electrical energy generation (incl. renewables, excl. photovoltaics) | |
460308. Pattern recognition | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Zhejiang University, China |
University of Tennessee, United States | |
University of Queensland | |
Hunan University, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7vz7/data-source-authentication-for-wide-area-synchrophasor-measurements-based-on-spatial-signature-extraction-and-quadratic-kernel-svm
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