IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i1p219-d127310.html
   My bibliography  Save this article

Study of Time and Meteorological Characteristics of Wind Speed Correlation in Flat Terrains Based on Operation Data

Author

Listed:
  • Xiaojun Shen

    (Department of Electrical Engineering, Tongji University, Shanghai 200092, China)

  • Chongcheng Zhou

    (Department of Electrical Engineering, Tongji University, Shanghai 200092, China)

  • Xuejiao Fu

    (Department of Electrical Engineering, Tongji University, Shanghai 200092, China)

Abstract

The accurate calculation and characteristic analysis of wind speed correlation (WSC) is the basis of wind farm equivalent modeling, wind power prediction and other advanced applications. It is well known that the accurate calculation of WSC depends on the quality of the raw data, and the WSC of wind turbines is related to spatial, time and meteorological conditions. However, the researches on the statistical analysis of time/meteorological WSC characteristics and the original data quality improvement for WSC calculation are rarely carried out. This paper reviews and redefines the concept and connotation of spatial, time and meteorological WSC. On this basis, a general process is proposed for WSC calculation including data classification, extraction and cleaning. Then the WSC characteristics between wind turbines are analyzed from time and meteorological dimensions based on the actual operation data. In addition, the influence of time WSC and meteorological WSC on wind turbine equivalent modeling and wind power prediction was discussed. The results of case study shows that the proposed general WSC calculation process is feasible and effective; the WSC for different time scales, wind speed ranges and wind directions varies greatly; the spatial WSC cannot characterize the time variability and directionality of the WSC. And the time and meteorological WSC characteristics are of great engineering value to improve the wind turbine equivalent modeling and wind power prediction accuracy, the influence of time scale and meteorological conditions should be considered in the applications of WSC.

Suggested Citation

  • Xiaojun Shen & Chongcheng Zhou & Xuejiao Fu, 2018. "Study of Time and Meteorological Characteristics of Wind Speed Correlation in Flat Terrains Based on Operation Data," Energies, MDPI, vol. 11(1), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:219-:d:127310
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/1/219/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/1/219/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sun, Can & Bie, Zhaohong & Xie, Min & Jiang, Jiangfeng, 2016. "Fuzzy copula model for wind speed correlation and its application in wind curtailment evaluation," Renewable Energy, Elsevier, vol. 93(C), pages 68-76.
    2. Chang, Tian-Pau & Liu, Feng-Jiao & Ko, Hong-Hsi & Huang, Ming-Chao, 2017. "Oscillation characteristic study of wind speed, global solar radiation and air temperature using wavelet analysis," Applied Energy, Elsevier, vol. 190(C), pages 650-657.
    3. Ye, Lin & Zhao, Yongning & Zeng, Cheng & Zhang, Cihang, 2017. "Short-term wind power prediction based on spatial model," Renewable Energy, Elsevier, vol. 101(C), pages 1067-1074.
    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. Xiaolu Chen & Ji Han & Tingting Zheng & Ping Zhang & Simo Duan & Shihong Miao, 2019. "A Vine-Copula Based Voltage State Assessment with Wind Power Integration," Energies, MDPI, vol. 12(10), pages 1-21, May.
    2. Xiaojun Shen & Chongchen Zhou & Guojie Li & Xuejiao Fu & Tek Tjing Lie, 2018. "Overview of Wind Parameters Sensing Methods and Framework of a Novel MCSPV Recombination Sensing Method for Wind Turbines," Energies, MDPI, vol. 11(7), pages 1-23, July.

    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. Huang, Yu & Zhang, Bingzhe & Pang, Huizhen & Wang, Biao & Lee, Kwang Y. & Xie, Jiale & Jin, Yupeng, 2022. "Spatio-temporal wind speed prediction based on Clayton Copula function with deep learning fusion," Renewable Energy, Elsevier, vol. 192(C), pages 526-536.
    2. Mojtaba Qolipour & Ali Mostafaeipour & Mohammad Saidi-Mehrabad & Hamid R Arabnia, 2019. "Prediction of wind speed using a new Grey-extreme learning machine hybrid algorithm: A case study," Energy & Environment, , vol. 30(1), pages 44-62, February.
    3. Yin, Yue & Liu, Tianqi & He, Chuan, 2019. "Day-ahead stochastic coordinated scheduling for thermal-hydro-wind-photovoltaic systems," Energy, Elsevier, vol. 187(C).
    4. Thiago B. Murari & Aloisio S. Nascimento Filho & Marcelo A. Moret & Sergio Pitombo & Alex A. B. Santos, 2020. "Self-Affine Analysis of ENSO in Solar Radiation," Energies, MDPI, vol. 13(18), pages 1-17, September.
    5. Jun Liu & Xudong Hao & Peifen Cheng & Wanliang Fang & Shuanbao Niu, 2016. "A Parallel Probabilistic Load Flow Method Considering Nodal Correlations," Energies, MDPI, vol. 9(12), pages 1-16, December.
    6. Ye, Lin & Zhang, Cihang & Xue, Hui & Li, Jiachen & Lu, Peng & Zhao, Yongning, 2019. "Study of assessment on capability of wind power accommodation in regional power grids," Renewable Energy, Elsevier, vol. 133(C), pages 647-662.
    7. Yu, Guangzheng & Liu, Chengquan & Tang, Bo & Chen, Rusi & Lu, Liu & Cui, Chaoyue & Hu, Yue & Shen, Lingxu & Muyeen, S.M., 2022. "Short term wind power prediction for regional wind farms based on spatial-temporal characteristic distribution," Renewable Energy, Elsevier, vol. 199(C), pages 599-612.
    8. Chiou-Jye Huang & Ping-Huan Kuo, 2018. "A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems," Energies, MDPI, vol. 11(10), pages 1-20, October.
    9. Heggarty, Thomas & Bourmaud, Jean-Yves & Girard, Robin & Kariniotakis, Georges, 2020. "Quantifying power system flexibility provision," Applied Energy, Elsevier, vol. 279(C).
    10. Konstantinos Konstas & Panos T. Chountalas & Eleni A. Didaskalou & Dimitrios A. Georgakellos, 2023. "A Pragmatic Framework for Data-Driven Decision-Making Process in the Energy Sector: Insights from a Wind Farm Case Study," Energies, MDPI, vol. 16(17), pages 1-26, August.
    11. Zhang, Tianyuan & Tan, Qian & Wang, Shuping & Zhang, Tong & Hu, Kejia & Zhang, Shan, 2022. "Assessment and management of composite risk in irrigated agriculture under water-food-energy nexus and uncertainty," Agricultural Water Management, Elsevier, vol. 262(C).
    12. Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2020. "Negative correlation learning-based RELM ensemble model integrated with OVMD for multi-step ahead wind speed forecasting," Renewable Energy, Elsevier, vol. 156(C), pages 804-819.
    13. Heggarty, Thomas & Bourmaud, Jean-Yves & Girard, Robin & Kariniotakis, Georges, 2019. "Multi-temporal assessment of power system flexibility requirement," Applied Energy, Elsevier, vol. 238(C), pages 1327-1336.
    14. Tawn, R. & Browell, J., 2022. "A review of very short-term wind and solar power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    15. Jia, Mengshuo & Huang, Shaowei & Wang, Zhiwen & Shen, Chen, 2021. "Privacy-preserving distributed parameter estimation for probability distribution of wind power forecast error," Renewable Energy, Elsevier, vol. 163(C), pages 1318-1332.
    16. Dong, Xinghui & Li, Jia & Gao, Di & Zheng, Kai, 2020. "Wind speed modeling for cascade clusters of wind turbines part 1: The cascade clusters of wind turbines," Energy, Elsevier, vol. 205(C).
    17. Wang, Jianzhou & Dong, Yunxuan & Zhang, Kequan & Guo, Zhenhai, 2017. "A numerical model based on prior distribution fuzzy inference and neural networks," Renewable Energy, Elsevier, vol. 112(C), pages 486-497.
    18. Wen, Yi & Kamranzad, Bahareh & Lin, Pengzhi, 2021. "Assessment of long-term offshore wind energy potential in the south and southeast coasts of China based on a 55-year dataset," Energy, Elsevier, vol. 224(C).
    19. Zheng, Ling & Zhou, Bin & Or, Siu Wing & Cao, Yijia & Wang, Huaizhi & Li, Yong & Chan, Ka Wing, 2021. "Spatio-temporal wind speed prediction of multiple wind farms using capsule network," Renewable Energy, Elsevier, vol. 175(C), pages 718-730.
    20. Li, Shuai & Ma, Hongjie & Li, Weiyi, 2017. "Typical solar radiation year construction using k-means clustering and discrete-time Markov chain," Applied Energy, Elsevier, vol. 205(C), pages 720-731.

    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:gam:jeners:v:11:y:2018:i:1:p:219-:d:127310. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.