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- W3167461903 abstract "This letter proposes a hybrid approach combining Self-Adaptive Mathematical Morphology (SAMM) and Time-Frequency (TF) techniques to authenticate the source information of Distribution Synchrophasors (DS) within near-range locations. The SAMM can adaptively regulate the synchrophasors variations which are representatives of local environmental characteristics. Subsequently, TF mapping is employed to extract informative signatures from the regulated synchrophasors variation. Finally, Random Forest Classification (RFC) is used to correlate the extracted signatures with the source information based on the derived TF mapping. Experiment results using DS collected at multiple small geographical scales validated the proposed methodology." @default.
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- W3167461903 date "2021-09-01" @default.
- W3167461903 modified "2023-10-14" @default.
- W3167461903 title "Source Authentication of Distribution Synchrophasors for Cybersecurity of Microgrids" @default.
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- W3167461903 doi "https://doi.org/10.1109/tsg.2021.3089041" @default.
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