Source Location Identification of Distribution-Level Electric Network Frequency Signals at Multiple Geographic Scales
Article
Article Title | Source Location Identification of Distribution-Level Electric Network Frequency Signals at Multiple Geographic Scales |
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ERA Journal ID | 210567 |
Article Category | Article |
Authors | Yao, Wenxuan (Author), Zhao, Jiecheng (Author), Till, Micah J. (Author), You, Shutang (Author), Liu, Yong (Author), Cui, Yi (Author) and Liu, Yilu (Author) |
Journal Title | IEEE Access |
Journal Citation | 5, pp. 11166-11175 |
Number of Pages | 10 |
Year | 2017 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 2169-3536 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2017.2707060 |
Web Address (URL) | https://ieeexplore.ieee.org/document/7932434 |
Abstract | The distribution-level electric network frequency (ENF) extracted from an electric power signal is a promising forensic tool for multimedia recording authentication. Local characteristics in ENF signals recorded in different locations act as environmental signatures, which can be potentially used as a fingerprint for location identification. In this paper, a reference database is established for distribution-level ENF using FNET/GridEye system. An ENF identification method that combines a wavelet-based signature extraction and feedforward artificial neural network-based machine learning is presented to identify the location of unsourced ENF signals without relying on the availability of concurrent signals. Experiments are performed to validate the effectiveness of the proposed method using ambient frequency measurements at multiple geographic scales. Identification accuracy is presented, and the factors that affect identification performance are discussed. |
Keywords | Distribution-level; ENF signal; frequency measurement; location identification; signature extraction |
ANZSRC Field of Research 2020 | 460403. Data security and protection |
400808. Photovoltaic power systems | |
400803. Electrical energy generation (incl. renewables, excl. photovoltaics) | |
460308. Pattern recognition | |
Byline Affiliations | University of Tennessee, United States |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7w37/source-location-identification-of-distribution-level-electric-network-frequency-signals-at-multiple-geographic-scales
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License: CC BY 4.0 | ||
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