IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i11p3034-d180615.html
   My bibliography  Save this article

Assessing the Factors Impacting on the Reliability of Wind Turbines via Survival Analysis—A Case Study

Author

Listed:
  • Samet Ozturk

    (Columbia University, 116th St & Broadway, New York, NY 10027, USA)

  • Vasilis Fthenakis

    (Columbia University, 116th St & Broadway, New York, NY 10027, USA)

  • Stefan Faulstich

    (Fraunhofer Institute for Energy Economics and Energy System Technology—IEE, Königstor 59, 34119 Kassel, Germany)

Abstract

The failure of wind turbines is a multi-faceted problem and its monetary impact is often unpredictable. In this study, we present a novel application of survival analysis on wind turbine reliability, including accounting for previous failures and the history of scheduled maintenance. We investigated the operational, climatic and geographical factors that affect wind turbine failure and modeled the risk rate of wind turbine failure based on data from 109 turbines in Germany operating for a period of 19 years. Our analysis showed that adequately scheduled maintenance can increase the survival of wind turbine systems and electric subsystems up to 2.8 and 3.8 times, respectively, compared to the systems without scheduled maintenance. Geared-drive wind turbines and their electrical systems were observed to have 1.2- and 1.4- times higher survival, respectively, compared to direct-drive turbines and their electrical systems. It was also found that the survival of frequently-failing wind turbine components, such as switches, was worse in geared-drive than in direct-drive wind turbines. We show that survival analysis is a useful tool to guide the reduction of the operating and maintenance costs of wind turbines.

Suggested Citation

  • Samet Ozturk & Vasilis Fthenakis & Stefan Faulstich, 2018. "Assessing the Factors Impacting on the Reliability of Wind Turbines via Survival Analysis—A Case Study," Energies, MDPI, vol. 11(11), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3034-:d:180615
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/11/3034/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/11/3034/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Amirat, Y. & Benbouzid, M.E.H. & Al-Ahmar, E. & Bensaker, B. & Turri, S., 2009. "A brief status on condition monitoring and fault diagnosis in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2629-2636, December.
    2. Peyman Mazidi & Lina Bertling Tjernberg & Miguel A Sanz Bobi, 2017. "Wind turbine prognostics and maintenance management based on a hybrid approach of neural networks and a proportional hazards model," Journal of Risk and Reliability, , vol. 231(2), pages 121-129, April.
    3. Kerres,, Bertrand & Fischer, Katharina & Madlener, Reinhard, 2014. "Economic Evaluation of Maintenance Strategies for Wind Turbines: A Stochastic Analysis," FCN Working Papers 3/2014, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    4. 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.
    5. 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.
    6. Reder, Maik & Yürüşen, Nurseda Y. & Melero, Julio J., 2018. "Data-driven learning framework for associating weather conditions and wind turbine failures," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 554-569.
    7. Symeon Christodoulou, 2011. "Water Network Assessment and Reliability Analysis by Use of Survival Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(4), pages 1229-1238, March.
    8. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
    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. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    2. Samet Ozturk & Vasilis Fthenakis, 2020. "Predicting Frequency, Time-To-Repair and Costs of Wind Turbine Failures," Energies, MDPI, vol. 13(5), pages 1-25, March.
    3. Juan Izquierdo & Adolfo Crespo Márquez & Jone Uribetxebarria & Asier Erguido, 2019. "Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance," Energies, MDPI, vol. 12(11), pages 1-17, May.
    4. Beata Bieszk-Stolorz & Iwona Markowicz, 2021. "Decline in Share Prices of Energy and Fuel Companies on the Warsaw Stock Exchange as a Reaction to the COVID-19 Pandemic," Energies, MDPI, vol. 14(17), pages 1-17, August.
    5. Jose R. Vargas-Jaramillo & Jhon A. Montanez-Barrera & Michael R. von Spakovsky & Lamine Mili & Sergio Cano-Andrade, 2019. "Effects of Producer and Transmission Reliability on the Sustainability Assessment of Power System Networks," Energies, MDPI, vol. 12(3), pages 1-21, February.

    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. Samet Ozturk & Vasilis Fthenakis & Stefan Faulstich, 2018. "Failure Modes, Effects and Criticality Analysis for Wind Turbines Considering Climatic Regions and Comparing Geared and Direct Drive Wind Turbines," Energies, MDPI, vol. 11(9), pages 1-18, September.
    2. 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.
    3. 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.
    4. McKenna, R. & Ostman v.d. Leye, P. & Fichtner, W., 2016. "Key challenges and prospects for large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1212-1221.
    5. 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).
    6. Giovanni Rinaldi & Philipp R. Thies & Lars Johanning, 2021. "Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 14(9), pages 1-28, April.
    7. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
    8. Abdollahzadeh, Hadi & Atashgar, Karim & Abbasi, Morteza, 2016. "Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups," Renewable Energy, Elsevier, vol. 88(C), pages 247-261.
    9. Brooks, Sam & Mahmood, Minhal & Roy, Rajkumar & Manolesos, Marinos & Salonitis, Konstantinos, 2023. "Self-reconfiguration simulations of turbines to reduce uneven farm degradation," Renewable Energy, Elsevier, vol. 206(C), pages 1301-1314.
    10. Liang, Jinping & Zhang, Ke & Al-Durra, Ahmed & Zhou, Daming, 2020. "A novel fault diagnostic method in power converters for wind power generation system," Applied Energy, Elsevier, vol. 266(C).
    11. Luis M. Abadie & Nestor Goicoechea, 2021. "Old Wind Farm Life Extension vs. Full Repowering: A Review of Economic Issues and a Stochastic Application for Spain," Energies, MDPI, vol. 14(12), pages 1-24, June.
    12. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2020. "Analysis of Wind Turbine Aging through Operation Curves," Energies, MDPI, vol. 13(21), pages 1-21, October.
    13. 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.
    14. Ravi Pandit & Davide Astolfi & Anh Minh Tang & David Infield, 2022. "Sequential Data-Driven Long-Term Weather Forecasting Models’ Performance Comparison for Improving Offshore Operation and Maintenance Operations," Energies, MDPI, vol. 15(19), pages 1-20, October.
    15. 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.
    16. Samet Ozturk & Vasilis Fthenakis, 2020. "Predicting Frequency, Time-To-Repair and Costs of Wind Turbine Failures," Energies, MDPI, vol. 13(5), pages 1-25, March.
    17. 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.
    18. Li, Jianlan & Zhang, Xuran & Zhou, Xing & Lu, Luyi, 2019. "Reliability assessment of wind turbine bearing based on the degradation-Hidden-Markov model," Renewable Energy, Elsevier, vol. 132(C), pages 1076-1087.
    19. Maria Martinez Luengo & Athanasios Kolios, 2015. "Failure Mode Identification and End of Life Scenarios of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 8(8), pages 1-16, August.
    20. Hong Wang & Hongbin Wang & Guoqian Jiang & Jimeng Li & Yueling Wang, 2019. "Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling," Energies, MDPI, vol. 12(6), pages 1-22, March.

    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:gam:jeners:v:11:y:2018:i:11:p:3034-:d:180615. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.