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A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks

Author

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  • Annalisa Santolamazza

    (DEIM School of Engineering, University of Tuscia, 01100 Viterbo, Italy)

  • Daniele Dadi

    (Department of Enterprise Engineering, University of Rome Tor Vergata, 00133 Rome, Italy)

  • Vito Introna

    (Department of Enterprise Engineering, University of Rome Tor Vergata, 00133 Rome, Italy)

Abstract

Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20–30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some of the main components of the wind turbine, such as gearbox and generator, and predict operating anomalies. The proposed method is tested on real wind turbines in Italy to verify its effectiveness and applicability, and it was demonstrated to be able to provide significant help for the maintenance of a wind farm.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1845-:d:524712
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    References listed on IDEAS

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    Cited by:

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    3. Galih Bangga, 2022. "Progress and Outlook in Wind Energy Research," Energies, MDPI, vol. 15(18), pages 1-5, September.
    4. Xiange Tian & Yongjian Jiang & Chen Liang & Cong Liu & You Ying & Hua Wang & Dahai Zhang & Peng Qian, 2022. "A Novel Condition Monitoring Method of Wind Turbines Based on GMDH Neural Network," Energies, MDPI, vol. 15(18), pages 1-15, September.
    5. Gang Li & Weidong Zhu, 2022. "A Review on Up-to-Date Gearbox Technologies and Maintenance of Tidal Current Energy Converters," Energies, MDPI, vol. 15(23), pages 1-24, December.
    6. Miriam Benedetti & Daniele Dadi & Lorena Giordano & Vito Introna & Pasquale Eduardo Lapenna & Annalisa Santolamazza, 2021. "Design of a Database of Case Studies and Technologies to Increase the Diffusion of Low-Temperature Waste Heat Recovery in the Industrial Sector," Sustainability, MDPI, vol. 13(9), pages 1-19, May.
    7. Daniel Chuquin-Vasco & Francis Parra & Nelson Chuquin-Vasco & Juan Chuquin-Vasco & Vanesa Lo-Iacono-Ferreira, 2021. "Prediction of Methanol Production in a Carbon Dioxide Hydrogenation Plant Using Neural Networks," Energies, MDPI, vol. 14(13), pages 1-18, July.
    8. Junshuai Yan & Yongqian Liu & Xiaoying Ren, 2023. "An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm," Energies, MDPI, vol. 16(10), pages 1-23, May.

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