Proactive Critical Energy Infrastructure Protection via Deep Feature Learning

Konstantina Fotiadou, Terpsichori Helen Velivassaki, Artemis Voulkidis, Dimitrios Skias, Corrado De Santis and Theodore Zahariadis
Additional contact information
Konstantina Fotiadou: Synelixis Solutions S.A, Farmakidou 10, GR34100 Chalkida, Greece
Terpsichori Helen Velivassaki: SingularLogic, Achaias 3 & Trizinias st., Kifisia, GR14564 Attica, Greece
Artemis Voulkidis: Synelixis Solutions S.A, Farmakidou 10, GR34100 Chalkida, Greece
Dimitrios Skias: Intrasoft International S.A.,2B Rue Nicolas Bové, L-1253 Luxembourg, Luxembourg
Corrado De Santis: BFP Group, Napoli 363/I, 70132 Bari, Italy
Theodore Zahariadis: Synelixis Solutions S.A, Farmakidou 10, GR34100 Chalkida, Greece

Energies, 2020, vol. 13, issue 10, 1-19

Abstract: 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.

Keywords: SCADA Anomaly Detection; cyberphysical systems; semi-supervised anomaly detection; sparse stacked autoencoders; deep feature learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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