IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i5p1063-d326419.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/5/1063/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/5/1063/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Helbing, Georg & Ritter, Matthias, 2018. "Deep Learning for fault detection in wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 189-198.
    2. Zhao, Hongshan & Liu, Huihai & Hu, Wenjing & Yan, Xihui, 2018. "Anomaly detection and fault analysis of wind turbine components based on deep learning network," Renewable Energy, Elsevier, vol. 127(C), pages 825-834.
    3. Martinez-Luengo, Maria & Kolios, Athanasios & Wang, Lin, 2016. "Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 91-105.
    4. Wang, Hongzhou, 2002. "A survey of maintenance policies of deteriorating systems," European Journal of Operational Research, Elsevier, vol. 139(3), pages 469-489, June.
    5. Sebastian Pfaffel & Stefan Faulstich & Kurt Rohrig, 2017. "Performance and Reliability of Wind Turbines: A Review," Energies, MDPI, vol. 10(11), pages 1-27, November.
    6. Liu, W.Y. & Tang, B.P. & Han, J.G. & Lu, X.N. & Hu, N.N. & He, Z.Z., 2015. "The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 466-472.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giovanni Rinaldi & Philipp R. Thies & Lars Johanning, 2021. "Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 14(9), pages 1-28, April.
    2. Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(C).
    3. Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.
    4. Adaiton Oliveira-Filho & Ryad Zemouri & Philippe Cambron & Antoine Tahan, 2023. "Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model," Energies, MDPI, vol. 16(12), pages 1-21, June.
    5. Cevasco, D. & Koukoura, S. & Kolios, A.J., 2021. "Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    6. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    7. Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
    8. Manisha Sawant & Sameer Thakare & A. Prabhakara Rao & Andrés E. Feijóo-Lorenzo & Neeraj Dhanraj Bokde, 2021. "A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics," Energies, MDPI, vol. 14(8), pages 1-30, April.
    9. Koukoura, Sofia & Scheu, Matti Niclas & Kolios, Athanasios, 2021. "Influence of extended potential-to-functional failure intervals through condition monitoring systems on offshore wind turbine availability," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    10. Wang, L. & Kolios, A. & Liu, X. & Venetsanos, D. & Rui, C., 2022. "Reliability of offshore wind turbine support structures: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    11. Thé, Jesse & Yu, Hesheng, 2017. "A critical review on the simulations of wind turbine aerodynamics focusing on hybrid RANS-LES methods," Energy, Elsevier, vol. 138(C), pages 257-289.
    12. Richmond, M. & Sobey, A. & Pandit, R. & Kolios, A., 2020. "Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning," Renewable Energy, Elsevier, vol. 161(C), pages 650-661.
    13. Pang, Yanhua & He, Qun & Jiang, Guoqian & Xie, Ping, 2020. "Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 161(C), pages 510-524.
    14. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
    15. Liu, Y. & Hajj, M. & Bao, Y., 2022. "Review of robot-based damage assessment for offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    16. Majdi Mansouri & Khaled Dhibi & Hazem Nounou & Mohamed Nounou, 2022. "An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization," Sustainability, MDPI, vol. 14(18), pages 1-11, September.
    17. Famoso, Fabio & Brusca, Sebastian & D'Urso, Diego & Galvagno, Antonio & Chiacchio, Ferdinando, 2020. "A novel hybrid model for the estimation of energy conversion in a wind farm combining wake effects and stochastic dependability," Applied Energy, Elsevier, vol. 280(C).
    18. Bakdi, Azzeddine & Kouadri, Abdelmalek & Mekhilef, Saad, 2019. "A data-driven algorithm for online detection of component and system faults in modern wind turbines at different operating zones," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 546-555.
    19. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
    20. Hashemi, M. & Asadi, M. & Zarezadeh, S., 2020. "Optimal maintenance policies for coherent systems with multi-type components," Reliability Engineering and System Safety, Elsevier, vol. 195(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1063-:d:326419. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.