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System safety analysis of large wind turbines

Author

Listed:
  • Jin, Xin
  • Ju, Wenbin
  • Zhang, Zhaolong
  • Guo, Lianxin
  • Yang, Xiangang

Abstract

Wind turbines are a proven source of clean energy with wind power energy harvesting technologies supplying about 3% of global electricity consumption. Consequently, the requirements and expectations of wind turbines keep increasing. However, due to the harsh operation environment of wind turbines, modern large wind turbines are subjected to different sort of failures. Thus, safety engineering is a critical issue for making wind energy competitive to conventional sources and achieving the desirable renewable targets. Researches in the safety engineering of wind turbines have gained dramatically increasing attention. Accordingly, this paper reviews the main basic research types and methods and their corresponding applications in system safety analysis, aiming to let more experts know the current research status and also provide guidance for relevant researches.

Suggested Citation

  • Jin, Xin & Ju, Wenbin & Zhang, Zhaolong & Guo, Lianxin & Yang, Xiangang, 2016. "System safety analysis of large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1293-1307.
  • Handle: RePEc:eee:rensus:v:56:y:2016:i:c:p:1293-1307
    DOI: 10.1016/j.rser.2015.12.016
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    1. Taflanidis, Alexandros A. & Loukogeorgaki, Eva & Angelides, Demos C., 2013. "Offshore wind turbine risk quantification/evaluation under extreme environmental conditions," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 19-32.
    2. Feng, Zhipeng & Liang, Ming & Zhang, Yi & Hou, Shumin, 2012. "Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation," Renewable Energy, Elsevier, vol. 47(C), pages 112-126.
    3. Kusiak, Andrew & Verma, Anoop, 2012. "Analyzing bearing faults in wind turbines: A data-mining approach," Renewable Energy, Elsevier, vol. 48(C), pages 110-116.
    4. Ji, Y.M. & Han, K.S., 2014. "Fracture mechanics approach for failure of adhesive joints in wind turbine blades," Renewable Energy, Elsevier, vol. 65(C), pages 23-28.
    5. Dong, Wenbin & Moan, Torgeir & Gao, Zhen, 2012. "Fatigue reliability analysis of the jacket support structure for offshore wind turbine considering the effect of corrosion and inspection," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 11-27.
    6. Pratumnopharat, Panu & Leung, Pak Sing & Court, Richard S., 2013. "Extracting fatigue damage parts from the stress–time history of horizontal axis wind turbine blades," Renewable Energy, Elsevier, vol. 58(C), pages 115-126.
    7. Dai, Lijuan & Ehlers, Sören & Rausand, Marvin & Utne, Ingrid Bouwer, 2013. "Risk of collision between service vessels and offshore wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 18-31.
    8. Tang, Baoping & Liu, Wenyi & Song, Tao, 2010. "Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution," Renewable Energy, Elsevier, vol. 35(12), pages 2862-2866.
    9. Li, Jimeng & Chen, Xuefeng & Du, Zhaohui & Fang, Zuowei & He, Zhengjia, 2013. "A new noise-controlled second-order enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis," Renewable Energy, Elsevier, vol. 60(C), pages 7-19.
    10. Liu, W.Y. & Zhang, W.H. & Han, J.G. & Wang, G.F., 2012. "A new wind turbine fault diagnosis method based on the local mean decomposition," Renewable Energy, Elsevier, vol. 48(C), pages 411-415.
    11. Kostandyan, Erik E. & Sørensen, John D., 2012. "Physics of failure as a basis for solder elements reliability assessment in wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 100-107.
    12. Zhang, Cai Wen & Zhang, Tieling & Chen, Nan & Jin, Tongdan, 2013. "Reliability modeling and analysis for a novel design of modular converter system of wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 86-94.
    13. Nielsen, Jannie Jessen & Sørensen, John Dalsgaard, 2011. "On risk-based operation and maintenance of offshore wind turbine components," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 218-229.
    14. Ashrafi, Maryam & Davoudpour, Hamid & Khodakarami, Vahid, 2015. "Risk assessment of wind turbines: Transition from pure mechanistic paradigm to modern complexity paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 347-355.
    15. Hameed, Z. & Vatn, J. & Heggset, J., 2011. "Challenges in the reliability and maintainability data collection for offshore wind turbines," Renewable Energy, Elsevier, vol. 36(8), pages 2154-2165.
    16. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
    17. Feng, Zhipeng & Liang, Ming, 2014. "Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time–frequency analysis," Renewable Energy, Elsevier, vol. 66(C), pages 468-477.
    18. 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.
    19. 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.
    20. Wenyi, Liu & Zhenfeng, Wang & Jiguang, Han & Guangfeng, Wang, 2013. "Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM," Renewable Energy, Elsevier, vol. 50(C), pages 1-6.
    21. Arifujjaman, Md. & Iqbal, M.T. & Quaicoe, J.E., 2009. "Reliability analysis of grid connected small wind turbine power electronics," Applied Energy, Elsevier, vol. 86(9), pages 1617-1623, September.
    22. Entezami, M. & Hillmansen, S. & Weston, P. & Papaelias, M.Ph., 2012. "Fault detection and diagnosis within a wind turbine mechanical braking system using condition monitoring," Renewable Energy, Elsevier, vol. 47(C), pages 175-182.
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    Cited by:

    1. Jin, Xin & Wang, Yaming & Ju, Wenbin & He, Jiao & Xie, Shuangyi, 2018. "Investigation into parameter influence of upstream deflector on vertical axis wind turbines output power via three-dimensional CFD simulation," Renewable Energy, Elsevier, vol. 115(C), pages 41-53.
    2. Hussain, Waqar & Khan, Sadia & Mover, Ather Hussain, 2022. "Development of quality, environment, health, and safety (QEHS) management system and its integration in operation and maintenance (O&M) of onshore wind energy industries," Renewable Energy, Elsevier, vol. 196(C), pages 220-233.
    3. 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.
    4. Yingning Qiu & Lang Chen & Yanhui Feng & Yili Xu, 2017. "An Approach of Quantifying Gear Fatigue Life for Wind Turbine Gearboxes Using Supervisory Control and Data Acquisition Data," Energies, MDPI, vol. 10(8), pages 1-21, July.
    5. Gianluca Pepe & Federica Mezzani & Antonio Carcaterra & Luca Cedola & Franco Rispoli, 2020. "Variational Control Approach to Energy Extraction from a Fluid Flow," Energies, MDPI, vol. 13(18), pages 1-20, September.
    6. Xue, Jie & Yip, Tsz Leung & Wu, Bing & Wu, Chaozhong & van Gelder, P.H.A.J.M., 2021. "A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: An application to a case in China," Renewable Energy, Elsevier, vol. 172(C), pages 897-917.
    7. Zhiyu Jiang & Weifei Hu & Wenbin Dong & Zhen Gao & Zhengru Ren, 2017. "Structural Reliability Analysis of Wind Turbines: A Review," Energies, MDPI, vol. 10(12), pages 1-25, December.

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