IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i12p4338-d184625.html
   My bibliography  Save this article

An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods

Author

Listed:
  • Lidong Zhang

    (School of Energy and Power Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Qikai Li

    (School of Energy and Power Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Yuanjun Guo

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Zhile Yang

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Lei Zhang

    (China Datang Corporation Renewable Science and Technology Research Institute, Beijing 10052, China)

Abstract

Wind direction and speed are both crucial factors for wind farm layout; however, the relationship between the two factors has not been well addressed. To optimize wind farm layout, this study aims to statistically explore wind speed characteristics under different wind directions and wind direction characteristics. For this purpose, the angular–linear model for approximating wind direction and speed characteristics were adopted and constructed with specified marginal distributions. Specifically, Weibull–Weibull distribution, lognormal–lognormal distribution and Weibull–lognormal distribution were applied to represent the marginal distribution of wind speed. Moreover, the finite mixture of von Mises function (FVMF) model was used to investigate the marginal distribution of wind direction. The parameters of those models were estimated by the expectation–maximum method. The optimal model was obtained by comparing the coefficient of determination value ( R 2 ) and Akaike’s information criteria (AIC). In the numerical study, wind data measured at a featured wind farm in north China was adopted. Results showed that the proposed joint distribution function could accurately represent the actual wind data at different heights, with the coefficient of determination value ( R 2 ) of 0.99.

Suggested Citation

  • Lidong Zhang & Qikai Li & Yuanjun Guo & Zhile Yang & Lei Zhang, 2018. "An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods," Sustainability, MDPI, vol. 10(12), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4338-:d:184625
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/12/4338/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/12/4338/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mathew, Sathyajith & Pandey, K.P. & Kumar.V, Anil, 2002. "Analysis of wind regimes for energy estimation," Renewable Energy, Elsevier, vol. 25(3), pages 381-399.
    2. Jamil, M. & Parsa, S. & Majidi, M., 1995. "Wind power statistics and an evaluation of wind energy density," Renewable Energy, Elsevier, vol. 6(5), pages 623-628.
    3. Shu, Z.R. & Li, Q.S. & Chan, P.W., 2015. "Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function," Applied Energy, Elsevier, vol. 156(C), pages 362-373.
    4. Islam, M.R. & Saidur, R. & Rahim, N.A., 2011. "Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function," Energy, Elsevier, vol. 36(2), pages 985-992.
    5. Haoran Zhao & Huiru Zhao & Sen Guo, 2018. "Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm," Sustainability, MDPI, vol. 10(3), pages 1-19, March.
    6. Masseran, N. & Razali, A.M. & Ibrahim, K. & Wan Zin, W.Z., 2012. "Evaluating the wind speed persistence for several wind stations in Peninsular Malaysia," Energy, Elsevier, vol. 37(1), pages 649-656.
    7. Jinliang Zhang & YiMing Wei & Zhong-fu Tan & Wang Ke & Wei Tian, 2017. "A Hybrid Method for Short-Term Wind Speed Forecasting," Sustainability, MDPI, vol. 9(4), pages 1-10, April.
    8. Pishgar-Komleh, S.H. & Keyhani, A. & Sefeedpari, P., 2015. "Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 313-322.
    9. Sedaghat, Ahmad & Hassanzadeh, Arash & Jamali, Jamaloddin & Mostafaeipour, Ali & Chen, Wei-Hsin, 2017. "Determination of rated wind speed for maximum annual energy production of variable speed wind turbines," Applied Energy, Elsevier, vol. 205(C), pages 781-789.
    10. Dai, Kaoshan & Bergot, Anthony & Liang, Chao & Xiang, Wei-Ning & Huang, Zhenhua, 2015. "Environmental issues associated with wind energy – A review," Renewable Energy, Elsevier, vol. 75(C), pages 911-921.
    11. Han, Qinkai & Hao, Zhuolin & Hu, Tao & Chu, Fulei, 2018. "Non-parametric models for joint probabilistic distributions of wind speed and direction data," Renewable Energy, Elsevier, vol. 126(C), pages 1032-1042.
    12. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    13. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang, 2016. "Wind speed probability distribution estimation and wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 881-899.
    14. Nima Amjady & Oveis Abedinia, 2017. "Short Term Wind Power Prediction Based on Improved Kriging Interpolation, Empirical Mode Decomposition, and Closed-Loop Forecasting Engine," Sustainability, MDPI, vol. 9(11), pages 1-18, November.
    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. Cheng Liu & Qinglan Li & Wei Zhao & Yuqing Wang & Riaz Ali & Dian Huang & Xiaoxiong Lu & Hui Zheng & Xiaolin Wei, 2020. "Spatiotemporal Characteristics of Near-Surface Wind in Shenzhen," Sustainability, MDPI, vol. 12(2), pages 1-19, January.
    2. Mekalathur B Hemanth Kumar & Saravanan Balasubramaniyan & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen, 2019. "Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India," Energies, MDPI, vol. 12(11), pages 1-21, June.
    3. Krzysztof Wrobel & Krzysztof Tomczewski & Artur Sliwinski & Andrzej Tomczewski, 2021. "Optimization of a Small Wind Power Plant for Annual Wind Speed Distribution," Energies, MDPI, vol. 14(6), pages 1-18, March.
    4. Sara Salvador & Riccardo Gatto, 2022. "Bayesian tests of symmetry for the generalized Von Mises distribution," Computational Statistics, Springer, vol. 37(2), pages 947-974, April.
    5. Mohammad Arashi & Priyanka Nagar & Andriette Bekker, 2020. "Joint Probabilistic Modeling of Wind Speed and Wind Direction for Wind Energy Analysis: A Case Study in Humansdorp and Noupoort," Sustainability, MDPI, vol. 12(11), pages 1-15, May.
    6. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.

    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. 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.
    2. Bagci, Kubra & Arslan, Talha & Celik, H. Eray, 2021. "Inverted Kumarswamy distribution for modeling the wind speed data: Lake Van, Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    3. Jiang, Haiyan & Wang, Jianzhou & Wu, Jie & Geng, Wei, 2017. "Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1199-1217.
    4. Alexandru Pîrjan & George Căruțașu & Dana-Mihaela Petroșanu, 2018. "Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain," Energies, MDPI, vol. 11(10), pages 1-42, October.
    5. Shu, Z.R. & Li, Q.S. & Chan, P.W., 2015. "Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function," Applied Energy, Elsevier, vol. 156(C), pages 362-373.
    6. Aliashim Albani & Mohd Zamri Ibrahim, 2017. "Wind Energy Potential and Power Law Indexes Assessment for Selected Near-Coastal Sites in Malaysia," Energies, MDPI, vol. 10(3), pages 1-21, March.
    7. He, Junyi & Chan, P.W. & Li, Qiusheng & Lee, C.W., 2020. "Spatiotemporal analysis of offshore wind field characteristics and energy potential in Hong Kong," Energy, Elsevier, vol. 201(C).
    8. Li, Yi & Wu, Xiao-Peng & Li, Qiu-Sheng & Tee, Kong Fah, 2018. "Assessment of onshore wind energy potential under different geographical climate conditions in China," Energy, Elsevier, vol. 152(C), pages 498-511.
    9. Gugliani, Gaurav Kumar & Sarkar, Arnab & Ley, Christophe & Matsagar, Vasant, 2021. "Identification of optimum wind turbine parameters for varying wind climates using a novel month-based turbine performance index," Renewable Energy, Elsevier, vol. 171(C), pages 902-914.
    10. Ali, Sajid & Lee, Sang-Moon & Jang, Choon-Man, 2018. "Statistical analysis of wind characteristics using Weibull and Rayleigh distributions in Deokjeok-do Island – Incheon, South Korea," Renewable Energy, Elsevier, vol. 123(C), pages 652-663.
    11. Fazelpour, Farivar & Markarian, Elin & Soltani, Nima, 2017. "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran," Renewable Energy, Elsevier, vol. 109(C), pages 646-667.
    12. Ayman Al-Quraan & Bashar Al-Mhairat, 2022. "Intelligent Optimized Wind Turbine Cost Analysis for Different Wind Sites in Jordan," Sustainability, MDPI, vol. 14(5), pages 1-24, March.
    13. Han, Qinkai & Wang, Tianyang & Chu, Fulei, 2022. "Nonparametric copula modeling of wind speed-wind shear for the assessment of height-dependent wind energy in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    14. Camilo Carrillo & José Cidrás & Eloy Díaz-Dorado & Andrés Felipe Obando-Montaño, 2014. "An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galicia (Spain)," Energies, MDPI, vol. 7(4), pages 1-25, April.
    15. Xu, Jin & Kanyingi, Peter Kairu & Wang, Keyou & Li, Guojie & Han, Bei & Jiang, Xiuchen, 2017. "Probabilistic small signal stability analysis with large scale integration of wind power considering dependence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1258-1270.
    16. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
    17. Nor, Khalid Mohamed & Shaaban, Mohamed & Abdul Rahman, Hasimah, 2014. "Feasibility assessment of wind energy resources in Malaysia based on NWP models," Renewable Energy, Elsevier, vol. 62(C), pages 147-154.
    18. Han, Qinkai & Chu, Fulei, 2021. "Directional wind energy assessment of China based on nonparametric copula models," Renewable Energy, Elsevier, vol. 164(C), pages 1334-1349.
    19. Ho, Lip-Wah, 2016. "Wind energy in Malaysia: Past, present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 279-295.
    20. Liu, Feng-Jiao & Chen, Pai-Hsun & Kuo, Shyi-Shiun & Su, De-Chuan & Chang, Tian-Pau & Yu, Yu-Hua & Lin, Tsung-Chi, 2011. "Wind characterization analysis incorporating genetic algorithm: A case study in Taiwan Strait," Energy, Elsevier, vol. 36(5), pages 2611-2619.

    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:jsusta:v:10:y:2018:i:12:p:4338-:d:184625. 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.