Anomaly Detection, Localization and Classification Using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units (PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms o...

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Veröffentlicht in:	IEEE transactions on smart grid Jg. 12; H. 4; S. 3570 - 3580
Hauptverfasser:	Ahmed, A., Sajan, K. S., Srivastava, A., Wu, Y.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Anomalies anomaly and event classification Anomaly detection Classification Data transmission Deep learning Distance learning Distributed generation Electric power grids Electric power systems Electrical loads Electricity distribution Energy sources Fault detection Heuristic algorithms Localization Machine learning Measuring instruments Multivariate analysis Online instruction Phasor measurement units Phasors Power system dynamics Real time Real-time systems Stress concentration Switching Training
ISSN:	1949-3053, 1949-3061
Online-Zugang:	Volltext
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