Multiscale Adaptive Multifractal Detrended Fluctuation Analysis-Based Source Identification of Synchrophasor Data
Article
Article Title | Multiscale Adaptive Multifractal Detrended Fluctuation Analysis-Based Source Identification of Synchrophasor Data |
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ERA Journal ID | 123777 |
Article Category | Article |
Authors | Cui, Yi (Author), Bai, Feifei (Author), Yin, Hongzhi (Author), Chen, Tong (Author), Dart, David (Author), Zillmann, Matthew (Author) and Ko, Ryan K. L. (Author) |
Journal Title | IEEE Transactions on Smart Grid |
Journal Citation | 13 (6), pp. 4957-4960 |
Number of Pages | 4 |
Year | 2022 |
Place of Publication | United States |
ISSN | 1949-3053 |
1949-3061 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/tsg.2022.3207066 |
Web Address (URL) | https://ieeexplore.ieee.org/document/9893184 |
Abstract | As a typical cyber-physical system, dispersed Phasor Measurement Units (PMUs) are networked together with advanced communication infrastructures to record the Distribution Synchrophasor (DS) which represents the states and dynamics of distribution power networks. Source information of DS is critical for many DS-based applications which is potentially vulnerable to data integrity attacks. To ensure the reliability of DS-based applications, it is imperative to efficiently authenticate the DS source locations before any DS data analytics is initiated. This letter presents a cost-effective method for accurate source identification by realising the multifractality of DS data. First, Multiscale Adaptive Multifractal Detrended Fluctuation Analysis (MSA-MFDFA) is executed to reveal the scale which possesses the most significant multifractality of the time-series DS. Subsequently, Adaptive Multifractal Interpolation (AMFI) is proposed to generate quasi high-resolution DS where unique time-frequency signatures are extracted. Such signatures are further fed into a deep learning model - deep forest for source identification. Experimental results using real-life DS of a distribution network illustrate the excellent performance of the proposed approach. |
Keywords | Source identification, synchrophasor, distribution power networks, cybersecurity, PMU |
ANZSRC Field of Research 2020 | 460403. Data security and protection |
400803. Electrical energy generation (incl. renewables, excl. photovoltaics) | |
460308. Pattern recognition | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | School of Engineering |
University of Queensland | |
NOJA Power, Australia | |
Energy Queensland, Australia | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7vzz/multiscale-adaptive-multifractal-detrended-fluctuation-analysis-based-source-identification-of-synchrophasor-data
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