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Analysis of SCADA data for early fault detection, with application to the maintenance management of wind turbines

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  • Bangalore, P.
  • Patriksson, M.

Abstract

Wind turbines are, generally, placed at remote locations and are subject to harsh environmental conditions throughout their lifetimes. Consequently, major failures in wind turbines are expensive to repair and cause losses of revenue due to long down times. Asset management using optimal maintenance strategies can aid in improving the reliability and the availability of wind turbines, thereby making them more competitive. Various mathematical optimization models for maintenance scheduling have been developed for application with wind turbines. Typically, these models provide either an age based or a condition based preventive maintenance schedule. This paper proposes a wind turbine maintenance management framework which utilizes operation and maintenance data from different sources to combine the benefits of age based and condition based maintenance scheduling. A mathematical model called Preventive Maintenance Scheduling Problem with Interval Costs (PMSPIC) is presented with modification for the maintenance optimization considering both age based and condition based failure rate models. The application of the maintenance management framework is demonstrated with case studies which illustrate the advantage of the proposed approach.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:115:y:2018:i:c:p:521-532
    DOI: 10.1016/j.renene.2017.08.073
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    Cited by:

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    3. Wu, Yueqi & Ma, Xiandong, 2022. "A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines," Renewable Energy, Elsevier, vol. 181(C), pages 554-566.
    4. 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.
    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. Cheng Xiao & Zuojun Liu & Tieling Zhang & Lei Zhang, 2019. "On Fault Prediction for Wind Turbine Pitch System Using Radar Chart and Support Vector Machine Approach," Energies, MDPI, vol. 12(14), pages 1-18, July.
    7. 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.
    8. Quanjiang Yu & Pramod Bangalore & Sara Fogelström & Serik Sagitov, 2024. "Optimal Preventive Maintenance Scheduling for Wind Turbines under Condition Monitoring," Energies, MDPI, vol. 17(2), pages 1-16, January.
    9. Jijian Lian & Ou Cai & Xiaofeng Dong & Qi Jiang & Yue Zhao, 2019. "Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development," Sustainability, MDPI, vol. 11(2), pages 1-29, January.
    10. 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.
    11. Yang, Wenguang & Liu, Chao & Jiang, Dongxiang, 2018. "An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring," Renewable Energy, Elsevier, vol. 127(C), pages 230-241.
    12. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.

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