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Investigation of energy output in mountain wind farm using multiple-units SCADA data

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  • Dai, Juchuan
  • Tan, Yayi
  • Shen, Xiangbin

Abstract

The energy output characteristics of mountain wind farms are more complex and unpredictable than that of flat wind farms because of complex mountainous terrain. Some questions about the energy output characteristics of mountain wind farms are still unclear. For example, the effect of local wind resource characteristics on the energy output of mountain wind farms. In another scenario, single-unit data from Supervisory Control and Data Acquisition (SCADA) system are used to investigate the performance of wind turbines in some cases. But, single-unit SCADA data may not be very comprehensive. To fill this gap, multiple-units SCADA data of 2 MW wind turbines are used to comparatively investigate the energy characteristics of wind turbines in a mountain wind farm. The main contribution of this paper is to obtain a new and comprehensive understanding of the actual characteristics of the energy output of mountain wind farms, rather than staying in the design stage or general theoretical analysis. Specifically, the following issues, including (1) influence of wind speed on energy output, (2) influence of wind direction on energy output, (3) relationship between rotor speed and energy output, (4) power coefficient, have been discussed in depth. To overcome the influence of inherent deviation in wind speed measurement and the inertia of the wind rotor, a newly improved algorithm for calculating power coefficient is proposed. Creatively, a fast and operable data dividing method is presented, and the power coefficients of different operating regions are given. Through the investigation, the influence of local wind resource characteristics on the energy output is obtained. It is found that in the same mountain wind farm, the energy output curves of different wind turbines are different. For instance, energy outputs for four wind turbines in December 2015 are 434 MW, 614 MW, 205 MW, 255 MW, respectively.

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  • Dai, Juchuan & Tan, Yayi & Shen, Xiangbin, 2019. "Investigation of energy output in mountain wind farm using multiple-units SCADA data," Applied Energy, Elsevier, vol. 239(C), pages 225-238.
    • Handle: RePEc:eee:appene:v:239:y:2019:i:c:p:225-238
    DOI: 10.1016/j.apenergy.2019.01.207
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    Cited by:

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    6. Han, Shuo & He, Mengjiao & Zhao, Ziwen & Chen, Diyi & Xu, Beibei & Jurasz, Jakub & Liu, Fusheng & Zheng, Hongxi, 2023. "Overcoming the uncertainty and volatility of wind power: Day-ahead scheduling of hydro-wind hybrid power generation system by coordinating power regulation and frequency response flexibility," Applied Energy, Elsevier, vol. 333(C).
    7. Radünz, William Corrêa & Sakagami, Yoshiaki & Haas, Reinaldo & Petry, Adriane Prisco & Passos, Júlio César & Miqueletti, Mayara & Dias, Eduardo, 2021. "Influence of atmospheric stability on wind farm performance in complex terrain," Applied Energy, Elsevier, vol. 282(PA).
    8. Lin, Zi & Liu, Xiaolei, 2020. "Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network," Energy, Elsevier, vol. 201(C).
    9. Carta, José A. & Díaz, Santiago & Castañeda, Alberto, 2020. "A global sensitivity analysis method applied to wind farm power output estimation models," Applied Energy, Elsevier, vol. 280(C).
    10. Dai, Juchuan & Li, Mimi & Chen, Huanguo & He, Tao & Zhang, Fan, 2022. "Progress and challenges on blade load research of large-scale wind turbines," Renewable Energy, Elsevier, vol. 196(C), pages 482-496.
    11. Wang, Anqi & Pei, Yan & Zhu, Yunyi & Qian, Zheng, 2023. "Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern," Renewable Energy, Elsevier, vol. 211(C), pages 918-937.

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