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Comparative analysis of degradation rates for inland and seaside wind turbines in compliance with the International Electrotechnical Commission standard

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  • Shin, Dongheon
  • Ko, Kyungnam

Abstract

In order to identify and compare annual degradation rates of wind turbines, power performance testing was experimentally carried out on the Hankyeong (seaside) and Seongsan (inland) wind farms of Jeju Island, Korea. Five to six years' worth of raw nacelle wind data from SCADA system were analyzed for this work. Power curves were drawn for each year according to IEC 61400-12-1. The AEP and the CF were calculated by applying the drawn power curves to Rayleigh distribution, after which the CF yearly degradation rate was estimated. This constituted the methodology for degradation analysis in this work. The accuracy of this methodology was verified by comparing the relative error between the two CF degradation rates. The second degradation rate was calculated by applying the methodology to the wind data corrected by Nacelle Transfer Function. As a result, CF degradation rates for Hankyeong wind turbines 5 to 9 were found to have a relative error of 5.5% on average. The average CF reduction rates of the Hankyeong and Seongsan wind turbines were revealed to be 0.320%/year and 0.118%/year, respectively. The power performance degradation was found to have been faster for seaside wind turbines than for those located further inland.

Suggested Citation

  • Shin, Dongheon & Ko, Kyungnam, 2017. "Comparative analysis of degradation rates for inland and seaside wind turbines in compliance with the International Electrotechnical Commission standard," Energy, Elsevier, vol. 118(C), pages 1180-1186.
    • Handle: RePEc:eee:energy:v:118:y:2017:i:c:p:1180-1186
    DOI: 10.1016/j.energy.2016.10.140
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    References listed on IDEAS

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    1. Al-Shammari, Eiman Tamah & Shamshirband, Shahaboddin & Petković, Dalibor & Zalnezhad, Erfan & Yee, Por Lip & Taher, Ros Suraya & Ćojbašić, Žarko, 2016. "Comparative study of clustering methods for wake effect analysis in wind farm," Energy, Elsevier, vol. 95(C), pages 573-579.
    2. Ko, Kyungnam & Kim, Kyoungbo & Huh, Jongchul, 2010. "Variations of wind speed in time on Jeju Island, Korea," Energy, Elsevier, vol. 35(8), pages 3381-3387.
    3. Oh, Hyunseok & Kim, Bumsuk, 2015. "Comparison and verification of the deviation between guaranteed and measured wind turbine power performance in complex terrain," Energy, Elsevier, vol. 85(C), pages 23-29.
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    5. 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.
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    Cited by:

    1. Mo, Huadong & Sansavini, Giovanni, 2019. "Impact of aging and performance degradation on the operational costs of distributed generation systems," Renewable Energy, Elsevier, vol. 143(C), pages 426-439.
    2. Dongheon Shin & Kyungnam Ko, 2019. "Application of the Nacelle Transfer Function by a Nacelle-Mounted Light Detection and Ranging System to Wind Turbine Power Performance Measurement," Energies, MDPI, vol. 12(6), pages 1-15, March.
    3. Saint-Drenan, Yves-Marie & Besseau, Romain & Jansen, Malte & Staffell, Iain & Troccoli, Alberto & Dubus, Laurent & Schmidt, Johannes & Gruber, Katharina & Simões, Sofia G. & Heier, Siegfried, 2020. "A parametric model for wind turbine power curves incorporating environmental conditions," Renewable Energy, Elsevier, vol. 157(C), pages 754-768.

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