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  4. Robust disturbance classification in power transmission systems with denoising recurrent autoencoders
 
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2022
Journal Article
Titel

Robust disturbance classification in power transmission systems with denoising recurrent autoencoders

Abstract
The automated classification of grid disturbances based on phasor measurements is a key technology for the reliable operation of power transmission systems. The predominant use of simulated training data limits the applicability of existing classification approaches due to the missing consideration of measurement errors or data quality issues. To mitigate these shortcomings, this study presents a robust disturbance classification procedure incorporating denoising recurrent autoencoders within a novel two-stage training approach. The developed disturbance classification procedure is evaluated for different noise characteristics and dataset combinations created with an optimization based error model. Experimental results based on a generic power transmission system show superior performance of the proposed two-stage design compared to a conventional, one-stage model training.
Author(s)
Kummerow, Andre
Institutsteil Angewandte Systemtechnik (AST) des Fraunhofer IOSB
Dirbas, Mohammad
Institutsteil Angewandte Systemtechnik (AST) des Fraunhofer IOSB
Monsalve, Cristian
Institutsteil Angewandte Systemtechnik (AST) des Fraunhofer IOSB
Nicolai, Steffen
Institutsteil Angewandte Systemtechnik (AST) des Fraunhofer IOSB
Bretschneider, Peter
Institutsteil Angewandte Systemtechnik (AST) des Fraunhofer IOSB
Zeitschrift
Sustainable energy, grids and networks
DOI
10.1016/j.segan.2022.100803
File(s)
avmain.pdf (1.75 MB)
Language
English
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Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Tags
  • Phasor measurements

  • Disturbance classific...

  • Recurrent neural netw...

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