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Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review

Author

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  • Md Liton Hossain

    (Department of Electrical and Computer Engineering, Curtin University, Kent Street, Bentley, Perth, WA 6102, Australia)

  • Ahmed Abu-Siada

    (Department of Electrical and Computer Engineering, Curtin University, Kent Street, Bentley, Perth, WA 6102, Australia)

  • S. M. Muyeen

    (Department of Electrical and Computer Engineering, Curtin University, Kent Street, Bentley, Perth, WA 6102, Australia)

Abstract

Condition monitoring and early fault diagnosis for wind turbines have become essential industry practice as they help improve wind farm reliability, overall performance and productivity. If not detected and rectified at early stages, some faults can be catastrophic with significant loss or revenue along with interruption to the business relying mainly on wind energy. The failure of Wind turbine results in system downtime and repairing or replacement expenses that significantly reduce the annual income. Such failures call for more systematized operation and maintenance schemes to ensure the reliability of wind energy conversion systems. Condition monitoring and fault diagnosis systems of wind turbine play an important role in reducing maintenance and operational costs and increase system reliability. This paper is aimed at providing the reader with the overall feature for wind turbine condition monitoring and fault diagnosis which includes various potential fault types and locations along with the signals to be analyzed with different signal processing methods.

Suggested Citation

  • Md Liton Hossain & Ahmed Abu-Siada & S. M. Muyeen, 2018. "Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review," Energies, MDPI, vol. 11(5), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1309-:d:148122
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    References listed on IDEAS

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    Cited by:

    1. 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.
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    4. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    5. Fernando Manuel Carvalho da Silva Santos & Leonardo Elizeire Bremermann & Tadeu Da Mata Medeiros Branco & Diego Issicaba & Mauro Augusto da Rosa, 2018. "Impact Evaluation of Wind Power Geographic Dispersion on Future Operating Reserve Needs," Energies, MDPI, vol. 11(11), pages 1-13, October.
    6. Pulivarthi Nageswara Rao & Ramesh Devarapalli & Fausto Pedro García Márquez & Hasmat Malik, 2020. "Global Sliding-Mode Suspension Control of Bearingless Switched Reluctance Motor under Eccentric Faults to Increase Reliability of Motor," Energies, MDPI, vol. 13(20), pages 1-38, October.
    7. Qian, XiaoYi & Sun, TianHe & Zhang, YuXian & Wang, BaoShi & Awad Gendeel, Mohammed Altayeb, 2023. "Wind turbine fault detection based on spatial-temporal feature and neighbor operation state," Renewable Energy, Elsevier, vol. 219(P1).
    8. Khazaee, Meghdad & Derian, Pierre & Mouraud, Anthony, 2022. "A comprehensive study on Structural Health Monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods," Renewable Energy, Elsevier, vol. 199(C), pages 1568-1579.
    9. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    10. Zhang, Shuo & Robinson, Emma & Basu, Malabika, 2022. "Hybrid Gaussian process regression and Fuzzy Inference System based approach for condition monitoring at the rotor side of a doubly fed induction generator," Renewable Energy, Elsevier, vol. 198(C), pages 936-946.
    11. Francesco Castellani & Luigi Garibaldi & Alessandro Paolo Daga & Davide Astolfi & Francesco Natili, 2020. "Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements," Energies, MDPI, vol. 13(6), pages 1-18, March.
    12. Bilal El Yousfi & Abdenour Soualhi & Kamal Medjaher & François Guillet, 2021. "A New Analytical Method for Modeling the Effect of Assembly Errors on a Motor-Gearbox System," Energies, MDPI, vol. 14(16), pages 1-21, August.
    13. Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    14. 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.
    15. Peng Guo & Jian Fu & XiYun Yang, 2018. "Condition Monitoring and Fault Diagnosis of Wind Turbines Gearbox Bearing Temperature Based on Kolmogorov-Smirnov Test and Convolutional Neural Network Model," Energies, MDPI, vol. 11(9), pages 1-16, August.
    16. Kong, Ziqian & Tang, Baoping & Deng, Lei & Liu, Wenyi & Han, Yan, 2020. "Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units," Renewable Energy, Elsevier, vol. 146(C), pages 760-768.
    17. 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).
    18. 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.
    19. Dong, Weiwei & Zhao, Guohua & Yüksel, Serhat & Dinçer, Hasan & Ubay, Gözde Gülseven, 2022. "A novel hybrid decision making approach for the strategic selection of wind energy projects," Renewable Energy, Elsevier, vol. 185(C), pages 321-337.
    20. Ruiming, Fang & Minling, Wu & xinhua, Guo & Rongyan, Shang & Pengfei, Shao, 2020. "Identifying early defects of wind turbine based on SCADA data and dynamical network marker," Renewable Energy, Elsevier, vol. 154(C), pages 625-635.
    21. Alejandro Blanco-M. & Pere Marti-Puig & Karina Gibert & Jordi Cusidó & Jordi Solé-Casals, 2019. "A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History," Energies, MDPI, vol. 12(10), pages 1-20, May.

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