IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v119y2018icp290-298.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148117312144
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2017.12.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    3. Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    2. KC, Anup & Whale, Jonathan & Urmee, Tania, 2019. "Urban wind conditions and small wind turbines in the built environment: A review," Renewable Energy, Elsevier, vol. 131(C), pages 268-283.
    3. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    4. Chen, Xin & Ye, Xiaoling & Xiong, Xiong & Zhang, Yingchao & Li, Yuanlu, 2024. "Improving the accuracy of wind speed spatial interpolation: A pre-processing algorithm for wind speed dynamic time warping interpolation," Energy, Elsevier, vol. 295(C).
    5. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    6. Coelho, Vitor N. & Weiss Cohen, Miri & Coelho, Igor M. & Liu, Nian & Guimarães, Frederico Gadelha, 2017. "Multi-agent systems applied for energy systems integration: State-of-the-art applications and trends in microgrids," Applied Energy, Elsevier, vol. 187(C), pages 820-832.
    7. KC, Anup & Whale, Jonathan & Evans, Samuel P. & Clausen, Philip D., 2020. "An investigation of the impact of wind speed and turbulence on small wind turbine operation and fatigue loads," Renewable Energy, Elsevier, vol. 146(C), pages 87-98.
    8. Li, Ranran & Jin, Yu, 2018. "A wind speed interval prediction system based on multi-objective optimization for machine learning method," Applied Energy, Elsevier, vol. 228(C), pages 2207-2220.
    9. Kirchner-Bossi, Nicolas & Kathari, Gabriel & Porté-Agel, Fernando, 2024. "A hybrid physics-based and data-driven model for intra-day and day-ahead wind power forecasting considering a drastically expanded predictor search space," Applied Energy, Elsevier, vol. 367(C).
    10. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
    11. LM López-Manrique & EV Macias-Melo & KM Aguilar-Castro & I Hernández-Pérez & HP Díaz-Hernández, 2021. "Review on methodological and normative advances in assessment and estimation of wind energy," Energy & Environment, , vol. 32(1), pages 25-61, February.
    12. Namiz Musafer & Nihal Samaratunga & P. G. Ajith Kumara, 2020. "The applications of appropriate renewable energy technologies by the refugees and displaced persons under humanitarian assistance programmes," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 4(12), pages 31-37, December.
    13. Aly, Hamed H.H., 2020. "A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting," Energy, Elsevier, vol. 213(C).
    14. Aly, Hamed H.H., 2022. "A Hybrid Optimized Model of Adaptive Neuro-Fuzzy Inference System, Recurrent Kalman Filter and Neuro-Wavelet for Wind Power Forecasting Driven by DFIG," Energy, Elsevier, vol. 239(PE).
    15. Rakib, M.I. & Evans, S.P. & Clausen, P.D., 2020. "Measured gust events in the urban environment, a comparison with the IEC standard," Renewable Energy, Elsevier, vol. 146(C), pages 1134-1142.
    16. Al-Ghandoor, Ahmed & Samhouri, Murad & Al-Hinti, Ismael & Jaber, Jamal & Al-Rawashdeh, Mohammad, 2012. "Projection of future transport energy demand of Jordan using adaptive neuro-fuzzy technique," Energy, Elsevier, vol. 38(1), pages 128-135.
    17. Sarah Jamal Mattar & Mohammad Reza Kavian Nezhad & Michael Versteege & Carlos F. Lange & Brian A. Fleck, 2021. "Validation Process for Rooftop Wind Regime CFD Model in Complex Urban Environment Using an Experimental Measurement Campaign," Energies, MDPI, vol. 14(9), pages 1-19, April.
    18. Mehrbakhsh Nilashi & Fausto Cavallaro & Abbas Mardani & Edmundas Kazimieras Zavadskas & Sarminah Samad & Othman Ibrahim, 2018. "Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    19. Qin, Li & Liu, Shi & Kang, Yi & Yan, Song An & Inaki Schlaberg, H. & Wang, Zhan, 2019. "Wind velocity distribution reconstruction using CFD database with Tucker decomposition and sensor measurement," Energy, Elsevier, vol. 167(C), pages 1236-1250.
    20. Liu, Ming-Lei & Ji, Qiang & Fan, Ying, 2013. "How does oil market uncertainty interact with other markets? An empirical analysis of implied volatility index," Energy, Elsevier, vol. 55(C), pages 860-868.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:119:y:2018:i:c:p:290-298. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.