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Combination of Thermal Modelling and Machine Learning Approaches for Fault Detection in Wind Turbine Gearboxes

Author

Listed:
  • Becky Corley

    (Wind Energy & Control Centre, University of Strathclyde, Glasgow G1 2TB, UK)

  • Sofia Koukoura

    (Wind Energy & Control Centre, University of Strathclyde, Glasgow G1 2TB, UK)

  • James Carroll

    (Wind Energy & Control Centre, University of Strathclyde, Glasgow G1 2TB, UK)

  • Alasdair McDonald

    (Institute for Energy Systems, University of Edinburgh, Edinburgh EH9 3DW, UK)

Abstract

This research aims to bring together thermal modelling and machine learning approaches to improve the understanding on the operation and fault detection of a wind turbine gearbox. Recent fault detection research has focused on machine learning, black box approaches. Although it can be successful, it provides no indication of the physical behaviour. In this paper, thermal network modelling was applied to two datasets using SCADA (Supervisory Control and Data Acquisition) temperature data, with the aim of detecting a fault one month before failure. A machine learning approach was used on the same data to compare the results to thermal modelling. The results found that thermal network modelling could successfully detect a fault in many of the turbines examined and was validated by the machine learning approach for one of the datasets. For that same dataset, it was found that combining the thermal model losses and the machine learning approach by using the modelled losses as a feature in the classifier resulted in the engineered feature becoming the most important feature in the classifier. It was also found that the results from thermal modelling had a significantly greater effect on successfully classifying the health of a turbine compared to temperature data. The other dataset gave less conclusive results, suggesting that the location of the fault and the temperature sensors could impact the fault-detection ability.

Suggested Citation

  • Becky Corley & Sofia Koukoura & James Carroll & Alasdair McDonald, 2021. "Combination of Thermal Modelling and Machine Learning Approaches for Fault Detection in Wind Turbine Gearboxes," Energies, MDPI, vol. 14(5), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1375-:d:509405
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    References listed on IDEAS

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    1. Pieter Nguyen Phuc & Hendrik Vansompel & Dimitar Bozalakov & Kurt Stockman & Guillaume Crevecoeur, 2019. "Inverse Thermal Identification of a Thermally Instrumented Induction Machine Using a Lumped-Parameter Thermal Model," Energies, MDPI, vol. 13(1), pages 1-27, December.
    2. 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.
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    Cited by:

    1. Alessandro Murgia & Robbert Verbeke & Elena Tsiporkova & Ludovico Terzi & Davide Astolfi, 2023. "Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis," Energies, MDPI, vol. 16(2), pages 1-20, January.
    2. Mingzhu Tang & Zixin Liang & Huawei Wu & Zimin Wang, 2021. "Fault Diagnosis Method for Wind Turbine Gearboxes Based on IWOA-RF," Energies, MDPI, vol. 14(19), pages 1-13, October.
    3. Behnam Firouzi & Khalid A. Alattas & Mohsen Bakouri & Abdullah K. Alanazi & Ardashir Mohammadzadeh & Saleh Mobayen & Afef Fekih, 2022. "A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines," Energies, MDPI, vol. 15(5), pages 1-19, February.
    4. Davide Astolfi & Ravi Pandit & Andrea Lombardi & Ludovico Terzi, 2022. "Multivariate Data-Driven Models for Wind Turbine Power Curves including Sub-Component Temperatures," Energies, MDPI, vol. 16(1), pages 1-18, December.
    5. 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.
    6. Davide Astolfi, 2023. "Wind Turbine Drivetrain Condition Monitoring through SCADA-Collected Temperature Data: Discussion of Selected Recent Papers," Energies, MDPI, vol. 16(9), pages 1-4, April.
    7. 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.
    8. Bruce Stephen, 2022. "Machine Learning Applications in Power System Condition Monitoring," Energies, MDPI, vol. 15(5), pages 1-2, March.

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