Multivariate time-series cyberattack detection in the distributed secondary control of AC microgrids with convolutional neural network autoencoder ensemble

This paper proposes an unsupervised machine learning-based approach for cyberattack detection in AC microgrids with distributed secondary control architecture. The proposed approach is fully unsupervised and only utilizes the system’s normal datasets for the training of the algorithm. The attack und...

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Veröffentlicht in:	Sustainable Energy, Grids and Networks Jg. 38; S. 101374
Hauptverfasser:	Roshanzadeh, Behshad, Choi, Jeewon, Bidram, Ali, Martínez-Ramón, Manel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.06.2024
Schlagworte:	Convolutional neural networks Cyberattack detection Deep learning Microgrid control Deep learning Convolutional neural networks Microgrid control Cyberattack detection
ISSN:	2352-4677, 2352-4677
Online-Zugang:	Volltext
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