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Frequency Distribution Model of Wind Speed Based on the Exponential Polynomial for Wind Farms

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  • Lingzhi Wang

    (School of Automation, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
    School of Automation and Information, Xi’an University of Technology, Xi’an 710048, China
    Shaanxi Key Laboratory of Complex System Control and Intelligent Information, Xi’an 710048, China)

  • Jun Liu

    (School of Automation and Information, Xi’an University of Technology, Xi’an 710048, China)

  • Fucai Qian

    (School of Automation and Information, Xi’an University of Technology, Xi’an 710048, China
    Autonomous Systems and Intelligent Control International Joint Research Center, Xi’an Technological University, Xi’an 710021, China)

Abstract

This study introduces and analyses existing models of wind speed frequency distribution in wind farms, such as the Weibull distribution model, the Rayleigh distribution model, and the lognormal distribution model. Inspired by the shortcomings of these models, we propose a distribution model based on an exponential polynomial, which can describe the actual wind speed frequency distribution. The fitting error of other common distribution models is too large at zero or low wind speeds. The proposed model can solve this problem. The exponential polynomial distribution model can fit multimodal distribution wind speed data as well as unimodal distribution wind speed data. We used the linear-least-squares method to acquire the parameters for the distribution model. Finally, we carried out contrast simulation experiments to validate the effectiveness and advantages of the proposed distribution model.

Suggested Citation

  • Lingzhi Wang & Jun Liu & Fucai Qian, 2019. "Frequency Distribution Model of Wind Speed Based on the Exponential Polynomial for Wind Farms," Sustainability, MDPI, vol. 11(3), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:3:p:665-:d:201224
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    References listed on IDEAS

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    5. Munir Ali Elfarra & Mustafa Kaya, 2018. "Comparison of Optimum Spline-Based Probability Density Functions to Parametric Distributions for the Wind Speed Data in Terms of Annual Energy Production," Energies, MDPI, vol. 11(11), pages 1-15, November.
    6. Katinas, Vladislovas & Gecevicius, Giedrius & Marciukaitis, Mantas, 2018. "An investigation of wind power density distribution at location with low and high wind speeds using statistical model," Applied Energy, Elsevier, vol. 218(C), pages 442-451.
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    Cited by:

    1. Dong, Zuo & Wang, Xianjia & Zhu, Runzhou & Dong, Xuan & Ai, Xueshan, 2022. "Improving the accuracy of wind speed statistical analysis and wind energy utilization in the Ningxia Autonomous Region, China," Applied Energy, Elsevier, vol. 320(C).
    2. Estefania Artigao & Antonio Vigueras-Rodríguez & Andrés Honrubia-Escribano & Sergio Martín-Martínez & Emilio Gómez-Lázaro, 2021. "Wind Resource and Wind Power Generation Assessment for Education in Engineering," Sustainability, MDPI, vol. 13(5), pages 1-27, February.
    3. Youming Cai & Zheng Li & Xu Cai, 2020. "Optimal Inertia Reserve and Inertia Control Strategy for Wind Farms," Energies, MDPI, vol. 13(5), pages 1-16, March.

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