Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • Publication
    Learning Demonstrator for Anomaly Detection in Distributed Energy Generation
    ( 2022-04-07)
    Pelchen, Timo
    ;
    Thiele, Gregor
    ;
    Vick, Axel
    ;
    Radke, Marcel
    ;
    Schade, David
    ;
    Krüger, Jörg
    Machine learning based anomaly detection methods on process data can be used to secure critical infrastructure. The design and installation of these methods require detailed understanding of both the facilities and the machine learning methods. Therefore, they are mostly incomprehensible for non-experts and thus acting as a barrier hindering the fast spread of such technologies. This article presents the systematic development of a demonstrator which enables presentations of anomaly detection on the example of a simulated wind farm. The specially designed user-interface allows a comprehensive experience. This article documents the use of the demonstrator for experts experienced in energy systems which are interested in the application of machine learning algorithms.