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Evaluation of Anomaly Detection of an Autoencoder Based on Maintenace Information and Scada-Data

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  • Marc-Alexander Lutz

    (Fraunhofer Institute for Energy Economics and Energy System Technology, Königstor 59, 34119 Kassel, Germany
    Current address: Fraunhofer Institute for Energy Economics and Energy System Technology, Königstor 59, 34119 Kassel, Germany.)

  • Stephan Vogt

    (Fraunhofer Institute for Energy Economics and Energy System Technology, Königstor 59, 34119 Kassel, Germany
    Current address: Fraunhofer Institute for Energy Economics and Energy System Technology, Königstor 59, 34119 Kassel, Germany.)

  • Volker Berkhout

    (Fraunhofer Institute for Energy Economics and Energy System Technology, Königstor 59, 34119 Kassel, Germany
    Current address: Fraunhofer Institute for Energy Economics and Energy System Technology, Königstor 59, 34119 Kassel, Germany.)

  • Stefan Faulstich

    (Fraunhofer Institute for Energy Economics and Energy System Technology, Königstor 59, 34119 Kassel, Germany
    Current address: Fraunhofer Institute for Energy Economics and Energy System Technology, Königstor 59, 34119 Kassel, Germany.)

  • Steffen Dienst

    (Trianel Windpark Borkum GmbH und Co. KG, Zirkusweg 2, 20359 Hamburg, Germany)

  • Urs Steinmetz

    (STEAG Energy Services GmbH, Rüttenscheider Str. 1-3, 45128 Essen, Germany)

  • Christian Gück

    (Fraunhofer Institute for Energy Economics and Energy System Technology, Königstor 59, 34119 Kassel, Germany
    Current address: Fraunhofer Institute for Energy Economics and Energy System Technology, Königstor 59, 34119 Kassel, Germany.)

  • Andres Ortega

    (Fraunhofer Institute for Energy Economics and Energy System Technology, Königstor 59, 34119 Kassel, Germany
    Current address: Fraunhofer Institute for Energy Economics and Energy System Technology, Königstor 59, 34119 Kassel, Germany.)

Abstract

The usage of machine learning techniques is widely spread and has also been implemented in the wind industry in the last years. Many of these techniques have shown great success but need to constantly prove the expectation of functionality. This paper describes a new method to monitor the health of a wind turbine using an undercomplete autoencoder. To evaluate the health monitoring quality of the autoencoder, the number of anomalies before an event has happened are to be considered. The results show that around 35% of all historical events that have resulted into a failure show many anomalies. Furthermore, the wind turbine subsystems which are subject to good detectability are the rotor system and the control system. If only one third of the service duties can be planned in advance, and thereby the scheduling time can be reduced, huge cost saving potentials can be seen.

Suggested Citation

  • Marc-Alexander Lutz & Stephan Vogt & Volker Berkhout & Stefan Faulstich & Steffen Dienst & Urs Steinmetz & Christian Gück & Andres Ortega, 2020. "Evaluation of Anomaly Detection of an Autoencoder Based on Maintenace Information and Scada-Data," Energies, MDPI, vol. 13(5), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1063-:d:326419
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & Charlie Plumley, 2021. "Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data," Energies, MDPI, vol. 14(20), pages 1-20, October.
    3. Panagiotis Korkos & Jaakko Kleemola & Matti Linjama & Arto Lehtovaara, 2022. "Representation Learning for Detecting the Faults in a Wind Turbine Hydraulic Pitch System Using Deep Learning," Energies, MDPI, vol. 15(24), pages 1-17, December.

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