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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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    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. Li, Jianlan & Zhang, Xuran & Zhou, Xing & Lu, Luyi, 2019. "Reliability assessment of wind turbine bearing based on the degradation-Hidden-Markov model," Renewable Energy, Elsevier, vol. 132(C), pages 1076-1087.
    3. Bangalore, P. & Patriksson, M., 2018. "Analysis of SCADA data for early fault detection, with application to the maintenance management of wind turbines," Renewable Energy, Elsevier, vol. 115(C), pages 521-532.
    4. Dong, Xinghui & Gao, Di & Li, Jia & Jincao, Zhang & Zheng, Kai, 2020. "Blades icing identification model of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 162(C), pages 575-586.
    5. 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.
    6. Yolanda Vidal & Francesc Pozo & Christian Tutivén, 2018. "Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data," Energies, MDPI, vol. 11(11), pages 1-18, November.
    7. Faiz, Jawad & Moosavi, S.M.M., 2016. "Eccentricity fault detection – From induction machines to DFIG—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 169-179.
    8. Yingying Zhao & Dongsheng Li & Ao Dong & Dahai Kang & Qin Lv & Li Shang, 2017. "Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data," Energies, MDPI, vol. 10(8), pages 1-17, August.
    9. Lei, Jinhao & Liu, Chao & Jiang, Dongxiang, 2019. "Fault diagnosis of wind turbine based on Long Short-term memory networks," Renewable Energy, Elsevier, vol. 133(C), pages 422-432.
    10. Adrian Stetco & Juan Melecio Ramirez & Anees Mohammed & Siniša Djurović & Goran Nenadic & John Keane, 2020. "An End-to-End, Real-Time Solution for Condition Monitoring of Wind Turbine Generators," Energies, MDPI, vol. 13(18), pages 1-18, September.
    11. Gisela Pujol-Vazquez & Leonardo Acho & José Gibergans-Báguena, 2020. "Fault Detection Algorithm for Wind Turbines’ Pitch Actuator Systems," Energies, MDPI, vol. 13(11), pages 1-14, June.
    12. Bi, Ran & Zhou, Chengke & Hepburn, Donald M., 2017. "Detection and classification of faults in pitch-regulated wind turbine generators using normal behaviour models based on performance curves," Renewable Energy, Elsevier, vol. 105(C), pages 674-688.
    13. Kusiak, Andrew & Verma, Anoop, 2012. "Analyzing bearing faults in wind turbines: A data-mining approach," Renewable Energy, Elsevier, vol. 48(C), pages 110-116.
    14. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "Models for monitoring wind farm power," Renewable Energy, Elsevier, vol. 34(3), pages 583-590.
    15. Manobel, Bartolomé & Sehnke, Frank & Lazzús, Juan A. & Salfate, Ignacio & Felder, Martin & Montecinos, Sonia, 2018. "Wind turbine power curve modeling based on Gaussian Processes and Artificial Neural Networks," Renewable Energy, Elsevier, vol. 125(C), pages 1015-1020.
    16. Yang, Xiyun & Zhang, Yanfeng & Lv, Wei & Wang, Dong, 2021. "Image recognition of wind turbine blade damage based on a deep learning model with transfer learning and an ensemble learning classifier," Renewable Energy, Elsevier, vol. 163(C), pages 386-397.
    17. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    18. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    19. Miriam Benedetti & Francesca Bonfà & Vito Introna & Annalisa Santolamazza & Stefano Ubertini, 2019. "Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications," Energies, MDPI, vol. 12(20), pages 1-28, October.
    20. Wenna Zhang & Xiandong Ma, 2016. "Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines," Energies, MDPI, vol. 9(4), pages 1-15, April.
    21. Yang, Hsu-Hao & Huang, Mei-Ling & Lai, Chun-Mei & Jin, Jhih-Rong, 2018. "An approach combining data mining and control charts-based model for fault detection in wind turbines," Renewable Energy, Elsevier, vol. 115(C), pages 808-816.
    22. Qian, Peng & Zhang, Dahai & Tian, Xiange & Si, Yulin & Li, Liangbi, 2019. "A novel wind turbine condition monitoring method based on cloud computing," Renewable Energy, Elsevier, vol. 135(C), pages 390-398.
    23. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
    24. Zuojun Liu & Cheng Xiao & Tieling Zhang & Xu Zhang, 2020. "Research on Fault Detection for Three Types of Wind Turbine Subsystems Using Machine Learning," Energies, MDPI, vol. 13(2), pages 1-21, January.
    25. Liu, Zepeng & Zhang, Long & Carrasco, Joaquin, 2020. "Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method," Renewable Energy, Elsevier, vol. 146(C), pages 99-110.
    26. Blanco, María Isabel, 2009. "The economics of wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1372-1382, August.
    27. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    28. Chang, Yuanhong & Chen, Jinglong & Qu, Cheng & Pan, Tongyang, 2020. "Intelligent fault diagnosis of Wind Turbines via a Deep Learning Network Using Parallel Convolution Layers with Multi-Scale Kernels," Renewable Energy, Elsevier, vol. 153(C), pages 205-213.
    29. 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.
    30. Ouyang, Tinghui & Kusiak, Andrew & He, Yusen, 2017. "Modeling wind-turbine power curve: A data partitioning and mining approach," Renewable Energy, Elsevier, vol. 102(PA), pages 1-8.
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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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