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A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

Author

Listed:
  • Peng Qian

    (Brunel Innovation Centre, Brunel University, London UB8 3PH, UK)

  • Xiange Tian

    (Brunel Innovation Centre, Brunel University, London UB8 3PH, UK)

  • Jamil Kanfoud

    (Brunel Innovation Centre, Brunel University, London UB8 3PH, UK)

  • Joash Lap Yan Lee

    (Brunel Innovation Centre, Brunel University, London UB8 3PH, UK)

  • Tat-Hean Gan

    (Brunel Innovation Centre, Brunel University, London UB8 3PH, UK
    TWI Ltd., Granta Park, Great Abington, Cambridge CB21 6AL, UK)

Abstract

Effective intelligent condition monitoring, as an effective technique to enhance the reliability of wind turbines and implement cost-effective maintenance, has been the object of extensive research and development to improve defect detection from supervisory control and data acquisition (SCADA) data, relying on perspective signal processing and statistical algorithms. The development of sophisticated machine learning now allows improvements in defect detection from historic data. This paper proposes a novel condition monitoring method for wind turbines based on Long Short-Term Memory (LSTM) algorithms. LSTM algorithms have the capability of capturing long-term dependencies hidden within a sequence of measurements, which can be exploited to increase the prediction accuracy. LSTM algorithms are therefore suitable for application in many diverse fields. The residual signal obtained by comparing the predicted values from a prediction model and the actual measurements from SCADA data can be used for condition monitoring. The effectiveness of the proposed method is validated in the case study. The proposed method can increase the economic benefits and reliability of wind farms.

Suggested Citation

  • Peng Qian & Xiange Tian & Jamil Kanfoud & Joash Lap Yan Lee & Tat-Hean Gan, 2019. "A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network," Energies, MDPI, vol. 12(18), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3411-:d:264049
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    References listed on IDEAS

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    Cited by:

    1. Jyh-Cherng Gu & Chun-Hung Liu & Kai-Ying Chou & Ming-Ta Yang, 2019. "Research on CBM of the Intelligent Substation SCADA System," Energies, MDPI, vol. 12(20), pages 1-22, October.
    2. Yuri Vankov & Aleksey Rumyantsev & Shamil Ziganshin & Tatyana Politova & Rinat Minyazev & Ayrat Zagretdinov, 2020. "Assessment of the Condition of Pipelines Using Convolutional Neural Networks," Energies, MDPI, vol. 13(3), pages 1-12, February.
    3. Ana Rita Nunes & Hugo Morais & Alberto Sardinha, 2021. "Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review," Energies, MDPI, vol. 14(21), pages 1-22, November.
    4. Yolanda Vidal, 2023. "Artificial Intelligence for Wind Turbine Condition Monitoring," Energies, MDPI, vol. 16(4), pages 1-4, February.
    5. 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.
    6. Ravi Kumar Pandit & Davide Astolfi & Isidro Durazo Cardenas, 2023. "A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines," Energies, MDPI, vol. 16(4), pages 1-17, February.
    7. Andre S. Barcelos & Antonio J. Marques Cardoso, 2021. "Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms," Energies, MDPI, vol. 14(9), pages 1-14, April.
    8. Mahsa Dehghan Manshadi & Majid Ghassemi & Seyed Milad Mousavi & Amir H. Mosavi & Levente Kovacs, 2021. "Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory," Energies, MDPI, vol. 14(16), pages 1-17, August.
    9. Li, Yanting & Jiang, Wenbo & Zhang, Guangyao & Shu, Lianjie, 2021. "Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data," Renewable Energy, Elsevier, vol. 171(C), pages 103-115.

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