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Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train

Author

Listed:
  • Estefania Artigao

    (Renewable Energy Research Institute (IIER) and DIEEAC-EDII-AB, University of Castilla—La Mancha, 02071 Albacete, Spain)

  • Sofia Koukoura

    (Department of Electric & Electronic Engineering, University of Strathclyde, Glasgow G11XV, UK)

  • Andrés Honrubia-Escribano

    (Renewable Energy Research Institute (IIER) and DIEEAC-EDII-AB, University of Castilla—La Mancha, 02071 Albacete, Spain)

  • James Carroll

    (Department of Electric & Electronic Engineering, University of Strathclyde, Glasgow G11XV, UK)

  • Alasdair McDonald

    (Department of Electric & Electronic Engineering, University of Strathclyde, Glasgow G11XV, UK)

  • Emilio Gómez-Lázaro

    (Renewable Energy Research Institute (IIER) and DIEEAC-EDII-AB, University of Castilla—La Mancha, 02071 Albacete, Spain)

Abstract

The goal of the present paper is to achieve the diagnosis of an in-service 1.5 MW wind turbine equipped with a doubly-fed induction generator through current signature and vibration analyses. Real data from operating machines have rarely been analysed in the scientific literature through current signature analysis supported by vibrations. The wind turbine under study was originally misdiagnosed by the operator, where a healthy component was replaced and the actual failure continued progressing. The chronological evolution of both the electrical current and vibration spectra is presented to conduct an in-depth tracking of the fault. The diagnosis is achieved through spectral analysis of the stator currents, where fault frequency components related to rotor mechanical unbalance are identified. This is confirmed by the vibration analysis, which provides insightful information on the health of the drive train. These results can be implemented in condition monitoring strategies, which is of great interest to optimise operation and maintenance costs of wind farms.

Suggested Citation

  • Estefania Artigao & Sofia Koukoura & Andrés Honrubia-Escribano & James Carroll & Alasdair McDonald & Emilio Gómez-Lázaro, 2018. "Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train," Energies, MDPI, vol. 11(4), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:960-:d:141601
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    References listed on IDEAS

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    Cited by:

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    3. Xiaocong Xiao & Jianxun Liu & Deshun Liu & Yufei Tang & Fan Zhang, 2022. "Condition Monitoring of Wind Turbine Main Bearing Based on Multivariate Time Series Forecasting," Energies, MDPI, vol. 15(5), pages 1-23, March.
    4. 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.
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    6. Koukoura, Sofia & Scheu, Matti Niclas & Kolios, Athanasios, 2021. "Influence of extended potential-to-functional failure intervals through condition monitoring systems on offshore wind turbine availability," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    7. Amina Bensalah & Georges Barakat & Yacine Amara, 2022. "Electrical Generators for Large Wind Turbine: Trends and Challenges," Energies, MDPI, vol. 15(18), pages 1-36, September.
    8. Petr Kacor & Petr Bernat & Petr Moldrik, 2021. "Utilization of Two Sensors in Offline Diagnosis of Squirrel-Cage Rotors of Asynchronous Motors," Energies, MDPI, vol. 14(20), pages 1-23, October.

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