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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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    1. Francesc Pozo & Yolanda Vidal & Josep M. Serrahima, 2016. "On Real-Time Fault Detection in Wind Turbines: Sensor Selection Algorithm and Detection Time Reduction Analysis," Energies, MDPI, vol. 9(7), pages 1-22, July.
    2. Yan Pei & Zheng Qian & Bo Jing & Dahai Kang & Lizhong Zhang, 2018. "Data-Driven Method for Wind Turbine Yaw Angle Sensor Zero-Point Shifting Fault Detection," Energies, MDPI, vol. 11(3), pages 1-14, March.
    3. Sinha, Y. & Steel, J.A., 2015. "A progressive study into offshore wind farm maintenance optimisation using risk based failure analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 735-742.
    4. Francesc Pozo & Yolanda Vidal & Óscar Salgado, 2018. "Wind Turbine Condition Monitoring Strategy through Multiway PCA and Multivariate Inference," Energies, MDPI, vol. 11(4), pages 1-19, March.
    5. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
    6. Pinar Pérez, Jesús María & García Márquez, Fausto Pedro & Tobias, Andrew & Papaelias, Mayorkinos, 2013. "Wind turbine reliability analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 463-472.
    7. Márquez, Fausto Pedro García & Pérez, Jesús María Pinar & Marugán, Alberto Pliego & Papaelias, Mayorkinos, 2016. "Identification of critical components of wind turbines using FTA over the time," Renewable Energy, Elsevier, vol. 87(P2), pages 869-883.
    8. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
    9. Wenna Zhang & Xiandong Ma, 2016. "Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines," Energies, MDPI, vol. 9(4), pages 1-15, April.
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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.
    5. 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.
    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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