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Published May 21, 2020 | Version v1
Journal article Open

Proactive Critical Energy Infrastructure Protection via Deep Feature Learning

  • 1. Synelixis Solutions S.A.
  • 2. SingularLogic
  • 3. Intrasoft International S.A.
  • 4. BFP Group

Description

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.

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Additional details

Funding

PHOENIX – Electrical Power System’s Shield against complex incidents and extensive cyber and privacy attacks 832989
European Commission