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Open AccessArticle

Proactive Critical Energy Infrastructure Protection via Deep Feature Learning

1
Synelixis Solutions S.A, Farmakidou 10, GR34100 Chalkida, Greece
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SingularLogic, Achaias 3 & Trizinias st., Kifisia, GR14564 Attica, Greece
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Intrasoft International S.A.,2B Rue Nicolas Bové, L-1253 Luxembourg, Luxembourg
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BFP Group, Napoli 363/I, 70132 Bari, Italy
*
Author to whom correspondence should be addressed.
Energies 2020, 13(10), 2622; https://doi.org/10.3390/en13102622
Received: 30 March 2020 / Revised: 8 May 2020 / Accepted: 14 May 2020 / Published: 21 May 2020
Autonomous fault detection plays a major role in the Critical Energy Infrastructure (CEI) domain, since sensor faults cause irreparable damage and lead to incorrect results on the condition monitoring of Cyber-Physical (CP) systems. This paper focuses on the challenging application of wind turbine (WT) monitoring. Specifically, we propose the two challenging architectures based on learning deep features, namely—Long Short Term Memory-Stacked Autoencoders (LSTM-SAE), and Convolutional Neural Network (CNN-SAE), for semi-supervised fault detection in wind CPs. The internal learnt features will facilitate the classification task by assigning each upcoming measurement into its corresponding faulty/normal operation status. To illustrate the quality of our schemes, their performance is evaluated against real-world’s wind turbine data. From the experimental section we are able to validate that both LSTM-SAE and CNN-SAE schemes provide high classification scores, indicating the high detection rate of the fault level of the wind turbines. Additionally, slight modification on our architectures are able to be applied on different fault/anomaly detection categories on variant Cyber-Physical systems. View Full-Text
Keywords: SCADA Anomaly Detection; cyberphysical systems; semi-supervised anomaly detection; sparse stacked autoencoders; deep feature learning SCADA Anomaly Detection; cyberphysical systems; semi-supervised anomaly detection; sparse stacked autoencoders; deep feature learning
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Fotiadou, K.; Velivassaki, T.H.; Voulkidis, A.; Skias, D.; De Santis, C.; Zahariadis, T. Proactive Critical Energy Infrastructure Protection via Deep Feature Learning. Energies 2020, 13, 2622.

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