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Health condition assessment of wind turbine generators based on supervisory control and data acquisition data

Health condition assessment of wind turbine generators based on supervisory control and data acquisition data

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As the running time of a wind turbine generator unit (WTGU) increases, the ageing and wear of its components will be aggravated gradually, which leads to deterioration of its operation condition. In order to ensure the safe and stable operation of WTGU and prolong its service life, it is of great significance to know the health condition of the WTGU and reasonably arrange maintenance. Based on the structure of the WTGU and the operation principle of the wind turbine generator (WTG), the condition assessment indexes of WTG were built, and the factors influencing the assessment index were determined. The relationship function between the assessment indexes and their influencing factors was established by using the least square support vector machine, which was used to determine the dynamic limitation of condition assessment index of WTG. The dynamic weight of each assessment index was used to characterise the influence of deterioration degree of different components on WTG conditions. Then the health conditions of WTG were evaluated using a similar cloud and fuzzy comprehensive assessment method separately. Finally, the proposed methods were verified by two examples including a direct-drive permanent magnet wind generator and a doubly-fed wind generator.

References

    1. 1)
      • 1. Global Wind Energy Council: ‘Global condition of wind power’. Available at http://gwec.net/global-figures/wind-energy-global-condition, accessed 25 April 2016.
    2. 2)
      • 2. Energy Research Institute National Development and Reform Commission: ‘China 2050 high renewable energy penetration scenario and roadmap study’, 2015.
    3. 3)
      • 3. Andrew, K., Wenyan, L.: ‘The prediction and diagnosis of wind turbine faults’, Renew. Energy, 2011, 36, (1), pp. 1623.
    4. 4)
      • 4. The European Wind Energy Association: ‘Wind power economics’, Publisher, 2010.
    5. 5)
      • 5. Tautz-Weinert, J., Watson, S.J.: ‘Using SCADA data for wind turbine condition monitoring–a review’, IET Renew. Power Gener., 2017, 11, (4), pp. 382394.
    6. 6)
      • 6. Yang, W., Tavner, P.J., Crabtree, C.J., et al: ‘Wind turbine condition monitoring: technical and commercial challenges’, Wind Energy, 2014, 17, (5), pp. 673693.
    7. 7)
      • 7. Tchakoua, P., Wamkeue, R., Ouhrouche, M., et al: ‘Wind turbine condition monitoring: state-of-the-art review, new trends, and future challenges’, Energies, 2014, 7, (4), pp. 25952630.
    8. 8)
      • 8. Chen, N., Yu, R., Chen, Y., et al: ‘Hierarchical method for wind turbine prognosis using SCADA data’, IET Renew. Power Gener., 2017, 11, (4), pp. 403410.
    9. 9)
      • 9. Lapira, E., Brisset, D., Ardakani, H.D., et al: ‘Wind turbine performance assessment using multi- regime modeling approach’, Renew. Energy, 2012, 45, pp. 8695.
    10. 10)
      • 10. Ruiming, F., Shunhui, J., Rongyan, S., et al: ‘Online wind turbine gearbox condition assessment cloud model using trend condition analysis’, J. Huaqiao Univ. Nat. Sci., 2016, 37, (1), pp. 3237.
    11. 11)
      • 11. Chang, S., Peng, G.: ‘Data preprocess of wind turbine based on least squares support vector machine and neighbor model’. 2017 29th Chinese Control and Decision Conf., Chongqing, China, May 2017, pp. 14411446.
    12. 12)
      • 12. Ranga1, C., Chandel, A.K., Chandel, R.: ‘Condition assessment of power transformers based on multi-attributes using fuzzy logic’, IET Sci. Meas. Technol., 2017, 11, (8), pp. 983990.
    13. 13)
      • 13. Li, H., Hu, Y.G., Yang, C., et al: ‘An improved fuzzy synthetic condition assessment of a wind turbine generator system’, Electr. Power Energy Syst., 2013, 45, pp. 468476.
    14. 14)
      • 14. Sun, P., Li, J., Wu, Y., et al: ‘Condition assessment of wind turbine generators based on cloud model’. IEEE Int. Conf. on Solid Dielectrics, Bologna, Italy, June 30–July 4 2013, pp. 146151.
    15. 15)
      • 15. Zheng, K., Han, L., Guo, S., et al: ‘Fuzzy synthetic condition assessment of wind turbine based on combination weighting and cloud model’, J. Intell. Fuzzy Syst., 2017, 32, pp. 45634572.
    16. 16)
      • 16. Timperley, J.E., Buchanan, D.W., Vallejo, J.M.: ‘Electric generation condition assessment with electromagnetic interference analysis’, IEEE Trans. Ind. Appl., 2018, 54, (2), pp. 19211929.
    17. 17)
      • 17. Li, J.-q., Wang, L.-h.: ‘Numerical simulation of temperature field in turbo-generators stator on cooling water blockage’, Proc. CSEE, 2009, 29, (12), pp. 7074.
    18. 18)
      • 18. Schlechtingen, M., Santos, I.F., Achiche, S.: ‘Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1:system description’, Appl. Soft Comput., 2013, 13, (1), pp. 259270.
    19. 19)
      • 19. Fitzgerald, A.E., Arthur, E.: ‘Electric machinery’ (McGraw-Hill, Boston, Mass, 2003).
    20. 20)
      • 20. Vaidya, O.S., Kumar, S.: ‘Analytic hierarchy process: an overview of application’, Eur. J. Oper. Res., 2006, 169, (1), pp. 129.
    21. 21)
      • 21. Saaty, T.L., Vargas, L.G.: ‘Models, methods, concepts and applications of the analytic hierarchy process’ (Springer, New York, USA, 2012).
    22. 22)
      • 22. Cammi, A., Misale, M., Devia, F., et al: ‘Stability analysis by means of information entropy assessment of a novel method against natural circulation experimental data’, Chem. Eng. Sci., 2017, l, (166), pp. 220234.
    23. 23)
      • 23. Jinping, H., Quan, G.: ‘An improved algorithm of cloud fusion for dam health diagnosis’, Geomatics Inf. Sci. Wuhan Univ., 2018, 43, (7), pp. 18.
    24. 24)
      • 24. Yahui, H., Mingjie, Z., Kui, W., et al: ‘Fuzzy comprehensive assessment of earth rockfill dam seepage security risk based on cloud model’, Water Resour. Power, 2018, 36, (3), pp. 8386.
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