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Nyquist-based adaptive sampling rate for wind measurement under varying wind conditions

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  • Korprasertsak, Natapol
  • Leephakpreeda, Thananchai

Abstract

In wind measurement, the sampling rate is one of the most crucial factors in wind data acquisition to yield good accuracy for wind analysis and wind resource assessment. To avoid aliasing problems, wind speed and direction are measured at a suitable sampling rate according to varying wind conditions. Basically, a high sampling rate is mostly preferable for wind speed measurement. However, when measurement at high sampling rate is performed, a great amount of data is obtained for storage and computation. Although the IEC 61400-12-1 standard recommends 10-min recording from measurement every second so as to reduce the amount of data, it is not able to provide satisfactory wind analysis. The Nyquist-based adaptive sampling rate method is proposed to adapt the sampling rate to be the Nyquist frequency according to actual wind conditions. In this study, the wind data at the high sampling rate of 10 Hz is determined, as a benchmark. The proposed methodology is capable of providing high accuracy of analytical results with percentage relative differences of less than 1% in wind analysis with the recommended parameters of the cut-out amplitude and cycle period. Also, the amount of wind data is significantly decreased by 4000 times from the benchmark.

Suggested Citation

  • Korprasertsak, Natapol & Leephakpreeda, Thananchai, 2018. "Nyquist-based adaptive sampling rate for wind measurement under varying wind conditions," Renewable Energy, Elsevier, vol. 119(C), pages 290-298.
    • Handle: RePEc:eee:renene:v:119:y:2018:i:c:p:290-298
    DOI: 10.1016/j.renene.2017.12.018
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    References listed on IDEAS

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    1. Coville, Aidan & Siddiqui, Afzal & Vogstad, Klaus-Ole, 2011. "The effect of missing data on wind resource estimation," Energy, Elsevier, vol. 36(7), pages 4505-4517.
    2. Tabrizi, Amir Bashirzadeh & Whale, Jonathan & Lyons, Thomas & Urmee, Tania, 2015. "Rooftop wind monitoring campaigns for small wind turbine applications: Effect of sampling rate and averaging period," Renewable Energy, Elsevier, vol. 77(C), pages 320-330.
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    4. Yang, Zhiling & Liu, Yongqian & Li, Chengrong, 2011. "Interpolation of missing wind data based on ANFIS," Renewable Energy, Elsevier, vol. 36(3), pages 993-998.
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    1. Korprasertsak, Natapol & Leephakpreeda, Thananchai, 2019. "Robust short-term prediction of wind power generation under uncertainty via statistical interpretation of multiple forecasting models," Energy, Elsevier, vol. 180(C), pages 387-397.

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