Výsledky vyhledávání - "Phillip Donner"

Investigating Machine Learning for Anomaly Detection in Phasor Measurement Unit Data

Autor: Jaemin Ashley, Adrian Barraza, Tyler Leary, Phillip Donner, Braden Blejski, Paul F. Evangelista, Connor McDonald, Christopher Hogue, Aaron St. Leger

Publikováno v: 2020 52nd North American Power Symposium (NAPS).

This paper investigates the use of Machine Learning (ML) algorithms for Anomaly Detection (AD) in Phasor Measurement Unit (PMU) data. Specifically, this is an evolution of previous heuristic techniques which investigated constraint and temporal anoma

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2ccd31f85456ebcfae7d9f7060deae10
https://doi.org/10.1109/naps50074.2021.9449638

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Unsupervised Machine Learning for Anomaly Detection in Synchrophasor Network Traffic

Autor: Aaron St. Leger, Phillip Donner, R. W. Blaine

Publikováno v: 2019 North American Power Symposium (NAPS).

In this paper, the k-means algorithm is applied to IEEE C37.118.2 synchrophasor network traffic data to model the expected packet features under normal operating conditions. Once the model is trained, anomalies in the data are introduced using packet

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::dff759a4a8715c183d2a22c6716f9990
https://doi.org/10.1109/naps46351.2019.9000400

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