IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v48y2015icp472-482.html
   My bibliography  Save this article

Maintenance management of wind turbines structures via MFCs and wavelet transforms

Author

Listed:
  • Ruiz de la Hermosa González-Carrato, Raúl
  • García Márquez, Fausto Pedro
  • Dimlaye, Vichaar

Abstract

This paper introduces a novel Fault Detection and Diagnosis method based on the wavelet transform to detect defects on the tower of a wind turbine. 24 Macro-Fiber Composite transducers have been placed to send and collect ultrasound signals. The data have been converted into voltage and analyzed by the wavelet transforms. Wavelet transform detects particular characteristics according to the shape or amplitude, and lead to diagnose imperfections in towers. Regarding to the wind turbines maintenance management, a large number of publications can be found focused on blades, mechanical, electrical/electronic devices, etc. However this trend is not extended to the wind turbine towers, and the development of their maintenance systems is not widespread yet. The results from this paper lead to acquire an early indication of structural or mechanical problems and it anticipates to catastrophic failures. It also allows a better preventive and predictive maintenance in wind turbines.

Suggested Citation

  • Ruiz de la Hermosa González-Carrato, Raúl & García Márquez, Fausto Pedro & Dimlaye, Vichaar, 2015. "Maintenance management of wind turbines structures via MFCs and wavelet transforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 472-482.
  • Handle: RePEc:eee:rensus:v:48:y:2015:i:c:p:472-482
    DOI: 10.1016/j.rser.2015.04.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032115002774
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2015.04.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yang, Bin & Sun, Dongbai, 2013. "Testing, inspecting and monitoring technologies for wind turbine blades: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 515-526.
    2. Pieraccini, Massimiliano & Parrini, Filippo & Fratini, Matteo & Atzeni, Carlo & Spinelli, Paolo, 2008. "In-service testing of wind turbine towers using a microwave sensor," Renewable Energy, Elsevier, vol. 33(1), pages 13-21.
    3. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    4. 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.
    5. Hansen, Anca D. & Michalke, Gabriele, 2007. "Fault ride-through capability of DFIG wind turbines," Renewable Energy, Elsevier, vol. 32(9), pages 1594-1610.
    6. Liu, Wenyi & Tang, Baoping & Jiang, Yonghua, 2010. "Status and problems of wind turbine structural health monitoring techniques in China," Renewable Energy, Elsevier, vol. 35(7), pages 1414-1418.
    7. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
    8. Jiang, Wenchun & Fan, Qinshan & Gong, Jianming, 2010. "Optimization of welding joint between tower and bottom flange based on residual stress considerations in a wind turbine," Energy, Elsevier, vol. 35(1), pages 461-467.
    9. Yang, Wenxian & Court, Richard & Jiang, Jiesheng, 2013. "Wind turbine condition monitoring by the approach of SCADA data analysis," Renewable Energy, Elsevier, vol. 53(C), pages 365-376.
    10. Soua, Slim & Van Lieshout, Paul & Perera, Asanka & Gan, Tat-Hean & Bridge, Bryan, 2013. "Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring," Renewable Energy, Elsevier, vol. 51(C), pages 175-181.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liu, W.Y., 2017. "A review on wind turbine noise mechanism and de-noising techniques," Renewable Energy, Elsevier, vol. 108(C), pages 311-320.
    2. Fausto Pedro García Marquez & Carlos Quiterio Gómez Muñoz, 2020. "A New Approach for Fault Detection, Location and Diagnosis by Ultrasonic Testing," Energies, MDPI, vol. 13(5), pages 1-13, March.
    3. Pliego Marugán, Alberto & García Márquez, Fausto Pedro & Pinar Pérez, Jesús María, 2022. "A techno-economic model for avoiding conflicts of interest between owners of offshore wind farms and maintenance suppliers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    4. Arcos Jiménez, Alfredo & Zhang, Long & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2020. "Maintenance management based on Machine Learning and nonlinear features in wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 316-328.
    5. García Márquez, Fausto Pedro & Peco Chacón, Ana María, 2020. "A review of non-destructive testing on wind turbines blades," Renewable Energy, Elsevier, vol. 161(C), pages 998-1010.
    6. Pliego Marugán, Alberto & Peco Chacón, Ana María & García Márquez, Fausto Pedro, 2019. "Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    7. Arcos Jiménez, Alfredo & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2019. "Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 2-12.
    8. Ruiz de la Hermosa González-Carrato, Raúl, 2017. "Sound and vibration-based pattern recognition for wind turbines driving mechanisms," Renewable Energy, Elsevier, vol. 109(C), pages 262-274.
    9. Carlos Quiterio Gómez Muñoz & Fausto Pedro García Márquez, 2016. "A New Fault Location Approach for Acoustic Emission Techniques in Wind Turbines," Energies, MDPI, vol. 9(1), pages 1-14, January.
    10. Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    11. Fausto Pedro García Márquez & Alberto Pliego Marugán & Jesús María Pinar Pérez & Stuart Hillmansen & Mayorkinos Papaelias, 2017. "Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines," Energies, MDPI, vol. 10(8), pages 1-19, July.
    12. Bakdi, Azzeddine & Kouadri, Abdelmalek & Mekhilef, Saad, 2019. "A data-driven algorithm for online detection of component and system faults in modern wind turbines at different operating zones," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 546-555.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jijian Lian & Ou Cai & Xiaofeng Dong & Qi Jiang & Yue Zhao, 2019. "Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development," Sustainability, MDPI, vol. 11(2), pages 1-29, January.
    2. Igba, Joel & Alemzadeh, Kazem & Durugbo, Christopher & Henningsen, Keld, 2015. "Performance assessment of wind turbine gearboxes using in-service data: Current approaches and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 144-159.
    3. Alberto Pliego Marugán & Fausto Pedro García Márquez & Jesús María Pinar Pérez, 2016. "Optimal Maintenance Management of Offshore Wind Farms," Energies, MDPI, vol. 9(1), pages 1-20, January.
    4. Yang, Ruizhen & He, Yunze & Zhang, Hong, 2016. "Progress and trends in nondestructive testing and evaluation for wind turbine composite blade," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1225-1250.
    5. Beganovic, Nejra & Söffker, Dirk, 2016. "Structural health management utilization for lifetime prognosis and advanced control strategy deployment of wind turbines: An overview and outlook concerning actual methods, tools, and obtained result," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 68-83.
    6. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
    7. 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.
    8. de Azevedo, Henrique Dias Machado & Araújo, Alex Maurício & Bouchonneau, Nadège, 2016. "A review of wind turbine bearing condition monitoring: State of the art and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 368-379.
    9. Colak, Ilhami & Fulli, Gianluca & Bayhan, Sertac & Chondrogiannis, Stamatios & Demirbas, Sevki, 2015. "Critical aspects of wind energy systems in smart grid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 155-171.
    10. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    11. Kang, Jichuan & Sun, Liping & Guedes Soares, C., 2019. "Fault Tree Analysis of floating offshore wind turbines," Renewable Energy, Elsevier, vol. 133(C), pages 1455-1467.
    12. Sungmok Hwang & Cheol Yoo, 2021. "Health Monitoring and Diagnosis System for a Small H-Type Darrieus Vertical-Axis Wind Turbine," Energies, MDPI, vol. 14(21), pages 1-18, November.
    13. Mérigaud, Alexis & Ringwood, John V., 2016. "Condition-based maintenance methods for marine renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 53-78.
    14. Igba, Joel & Alemzadeh, Kazem & Durugbo, Christopher & Eiriksson, Egill Thor, 2016. "Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes," Renewable Energy, Elsevier, vol. 91(C), pages 90-106.
    15. Chen, Junsheng & Li, Jian & Chen, Weigen & Wang, Youyuan & Jiang, Tianyan, 2020. "Anomaly detection for wind turbines based on the reconstruction of condition parameters using stacked denoising autoencoders," Renewable Energy, Elsevier, vol. 147(P1), pages 1469-1480.
    16. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    17. Teng, Wei & Ding, Xian & Zhang, Xiaolong & Liu, Yibing & Ma, Zhiyong, 2016. "Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform," Renewable Energy, Elsevier, vol. 93(C), pages 591-598.
    18. Rodríguez-López, Miguel A. & López-González, Luis M. & López-Ochoa, Luis M. & Las-Heras-Casas, Jesús, 2016. "Development of indicators for the detection of equipment malfunctions and degradation estimation based on digital signals (alarms and events) from operation SCADA," Renewable Energy, Elsevier, vol. 99(C), pages 224-236.
    19. 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.
    20. Seyed Abolfazl Mortazavizadeh & Reza Yazdanpanah & David Campos Gaona & Olimpo Anaya-Lara, 2023. "Fault Diagnosis and Condition Monitoring in Wave Energy Converters: A Review," Energies, MDPI, vol. 16(19), pages 1-16, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:48:y:2015:i:c:p:472-482. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.