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Application of the Nacelle Transfer Function by a Nacelle-Mounted Light Detection and Ranging System to Wind Turbine Power Performance Measurement

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  • Dongheon Shin

    (Multidisciplinary Graduate School Program for Wind Energy, Jeju National University, 102 Jejudaehakro, Jeju 63243, Korea)

  • Kyungnam Ko

    (Faculty of Wind Energy Engineering, Graduate School, Jeju National University, 102 Jejudaehakro, Jeju 63243, Korea)

Abstract

To examine the applicability of the nacelle transfer function (NTF) derived from nacelle light detection and ranging (LIDAR) measurements to wind turbine power performance testing without a met mast, wind turbine power performance measurement was carried out at the Dongbok wind farm on Jeju Island, South Korea. A nacelle LIDAR was mounted on the nacelle of a 2-MW wind turbine to measure wind conditions in front of the turbine rotor, and an 80-m-high met mast was installed near another wind turbine to measure the free-stream wind speed. The power measurement instruments were installed in the turbine tower base, and wind speeds measured by the nacelle anemometer of the turbine were collected by the SCADA (Supervisory control and data acquisition) system. The NTF was determined by the table method, and then the power curve drawn using the NTF by the nacelle LIDAR (PC NTF, NL ) was compared with the power curves drawn in compliance with International Electrotechnical Commission (IEC) standards, 61400-12-1 and 61400-12-2. Next, the combined standard uncertainties of the power curves were calculated to clarify the magnitude of the components of the uncertainties. The uncertainties of annual energy production (AEP) were also estimated by assuming that wind speed is a Rayleigh cumulative distribution. As a result, the PC NTF, NL was in good agreement with the power curves drawn in accordance with the IEC standards. The combined standard uncertainty of PC NTF, NL was almost the same as that of the power curve based on IEC 61400-12-2.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1087-:d:215893
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    References listed on IDEAS

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    Cited by:

    1. Yanhui Qiao & Yongqian Liu & Yang Chen & Shuang Han & Luo Wang, 2022. "Power Generation Performance Indicators of Wind Farms Including the Influence of Wind Energy Resource Differences," Energies, MDPI, vol. 15(5), pages 1-25, February.
    2. Xiaodong Wang & Yunong Liu & Luyao Wang & Lin Ding & Hui Hu, 2019. "Numerical Study of Nacelle Wind Speed Characteristics of a Horizontal Axis Wind Turbine under Time-Varying Flow," Energies, MDPI, vol. 12(20), pages 1-19, October.
    3. Shin, Dongheon & Ko, Kyungnam, 2022. "Experimental study on application of nacelle-mounted LiDAR for analyzing wind turbine wake effects by distance," Energy, Elsevier, vol. 243(C).
    4. 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.
    5. Jing Zhang & Jixing Chen & Hao Liu & Yining Chen & Jingwen Yang & Zongtao Yuan & Qingan Li, 2023. "Applicability of WorldCover in Wind Power Engineering: Application Research of Coupled Wake Model Based on Practical Project," Energies, MDPI, vol. 16(5), pages 1-16, February.
    6. Mohsen Vahidzadeh & Corey D. Markfort, 2020. "An Induction Curve Model for Prediction of Power Output of Wind Turbines in Complex Conditions," Energies, MDPI, vol. 13(4), pages 1-23, February.
    7. Mohsen Vahidzadeh & Corey D. Markfort, 2019. "Modified Power Curves for Prediction of Power Output of Wind Farms," Energies, MDPI, vol. 12(9), pages 1-19, May.

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