IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i5p863-d211103.html
   My bibliography  Save this article

Study of Generalized Interaction Wake Models Systems with ELM Variation for Off-Shore Wind Farms

Author

Listed:
  • Mingcan Li

    (School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
    These authors contributed equally to this work.)

  • Hanbin Xiao

    (School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
    These authors contributed equally to this work.)

  • Lin Pan

    (School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
    National Engineering Research Center for Water Transport Safety (WTS Center), Wuhan 430063, China
    State Key Laboratory of Ocean Engineering, Shanghai Jiaotong University, Shanghai 200240, China)

  • Chengjun Xu

    (School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
    These authors contributed equally to this work.)

Abstract

This paper reports a novel frandsen generalized wake model and its variation model-frandsen generalized normal distribution wake model for off-shore wind farms. Two different new wake models in off-shore wind farms have been studied comparatively. Their characteristics have been analyzed through mathematical modeling and derivation. Meanwhile, simulation experiments show that the proposed two new wake models have different properties. Furthermore, the distributions of wind speed and wind direction are modeled by the statistical methods and Extreme Learning Machine through the off-shore wind farms of Yangshan Deepwater Harbor in the Port of Shanghai, China. In addition, the data of wind energy are provided to verify and test the correctness and effectiveness of the proposed two models. Wind power has been demonstrated by wind rose and wind resources with real-time data. These techniques contribute to enhance planning, utilization and exploitation for wind power of off-shore wind farms.

Suggested Citation

  • Mingcan Li & Hanbin Xiao & Lin Pan & Chengjun Xu, 2019. "Study of Generalized Interaction Wake Models Systems with ELM Variation for Off-Shore Wind Farms," Energies, MDPI, vol. 12(5), pages 1-32, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:863-:d:211103
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/5/863/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/5/863/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shakoor, Rabia & Hassan, Mohammad Yusri & Raheem, Abdur & Wu, Yuan-Kang, 2016. "Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1048-1059.
    2. Pookpunt, Sittichoke & Ongsakul, Weerakorn, 2013. "Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients," Renewable Energy, Elsevier, vol. 55(C), pages 266-276.
    3. Tiny Remmers & Fiona Cawkwell & Cian Desmond & Jimmy Murphy & Eirini Politi, 2019. "The Potential of Advanced Scatterometer (ASCAT) 12.5 km Coastal Observations for Offshore Wind Farm Site Selection in Irish Waters," Energies, MDPI, vol. 12(2), pages 1-16, January.
    4. Clemens Hübler & Wout Weijtjens & Cristian G. Gebhardt & Raimund Rolfes & Christof Devriendt, 2019. "Validation of Improved Sampling Concepts for Offshore Wind Turbine Fatigue Design," Energies, MDPI, vol. 12(4), pages 1-20, February.
    5. Li, Hongmin & Wang, Jianzhou & Lu, Haiyan & Guo, Zhenhai, 2018. "Research and application of a combined model based on variable weight for short term wind speed forecasting," Renewable Energy, Elsevier, vol. 116(PA), pages 669-684.
    6. Ebrahimi, F.M. & Khayatiyan, A. & Farjah, E., 2016. "A novel optimizing power control strategy for centralized wind farm control system," Renewable Energy, Elsevier, vol. 86(C), pages 399-408.
    7. Chen, Jinjin, 2011. "Development of offshore wind power in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 5013-5020.
    8. Ping He & Seyed Ali Arefifar & Congshan Li & Fushuan Wen & Yuqi Ji & Yukun Tao, 2019. "Enhancing Oscillation Damping in an Interconnected Power System with Integrated Wind Farms Using Unified Power Flow Controller," Energies, MDPI, vol. 12(2), pages 1-16, January.
    9. Park, Jinkyoo & Law, Kincho H., 2016. "A data-driven, cooperative wind farm control to maximize the total power production," Applied Energy, Elsevier, vol. 165(C), pages 151-165.
    10. Chaurasiya, Prem Kumar & Ahmed, Siraj & Warudkar, Vilas, 2018. "Comparative analysis of Weibull parameters for wind data measured from met-mast and remote sensing techniques," Renewable Energy, Elsevier, vol. 115(C), pages 1153-1165.
    11. Tanvir Ahmad & Abdul Basit & Juveria Anwar & Olivier Coupiac & Behzad Kazemtabrizi & Peter C. Matthews, 2019. "Fast Processing Intelligent Wind Farm Controller for Production Maximisation," Energies, MDPI, vol. 12(3), pages 1-17, February.
    12. Zhixin, Wang & Chuanwen, Jiang & Qian, Ai & Chengmin, Wang, 2009. "The key technology of offshore wind farm and its new development in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 216-222, January.
    13. Suganthi, S.T. & Devaraj, D. & Ramar, K. & Hosimin Thilagar, S., 2018. "An Improved Differential Evolution algorithm for congestion management in the presence of wind turbine generators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 635-642.
    14. Romanic, Djordje & Parvu, Dan & Refan, Maryam & Hangan, Horia, 2018. "Wind and tornado climatologies and wind resource modelling for a modern development situated in “Tornado Alley”," Renewable Energy, Elsevier, vol. 115(C), pages 97-112.
    15. Song, Zhe & Zhang, Zijun & Chen, Xingying, 2016. "The decision model of 3-dimensional wind farm layout design," Renewable Energy, Elsevier, vol. 85(C), pages 248-258.
    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. Pan, Lin & Shao, Chengpeng, 2020. "Wind energy conversion systems analysis of PMSG on offshore wind turbine using improved SMC and Extended State Observer," Renewable Energy, Elsevier, vol. 161(C), pages 149-161.
    2. Pan, Lin & Wang, Xudong, 2020. "Variable pitch control on direct-driven PMSG for offshore wind turbine using Repetitive-TS fuzzy PID control," Renewable Energy, Elsevier, vol. 159(C), pages 221-237.

    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. Sun, Haiying & Yang, Hongxing & Gao, Xiaoxia, 2019. "Investigation into spacing restriction and layout optimization of wind farm with multiple types of wind turbines," Energy, Elsevier, vol. 168(C), pages 637-650.
    2. Azlan, F. & Kurnia, J.C. & Tan, B.T. & Ismadi, M.-Z., 2021. "Review on optimisation methods of wind farm array under three classical wind condition problems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    3. Kyoungboo Yang & Kyungho Cho, 2019. "Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study," Energies, MDPI, vol. 12(23), pages 1-15, November.
    4. Tanvir Ahmad & Abdul Basit & Muneeb Ahsan & Olivier Coupiac & Nicolas Girard & Behzad Kazemtabrizi & Peter C. Matthews, 2019. "Implementation and Analyses of Yaw Based Coordinated Control of Wind Farms," Energies, MDPI, vol. 12(7), pages 1-15, April.
    5. Lo Brutto, Ottavio A. & Nguyen, Van Thinh & Guillou, Sylvain S. & Thiébot, Jérôme & Gualous, Hamid, 2016. "Tidal farm analysis using an analytical model for the flow velocity prediction in the wake of a tidal turbine with small diameter to depth ratio," Renewable Energy, Elsevier, vol. 99(C), pages 347-359.
    6. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2014. "Investigation into the optimal wind turbine layout patterns for a Hong Kong offshore wind farm," Energy, Elsevier, vol. 73(C), pages 430-442.
    7. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    8. Davide Astolfi & Francesco Castellani, 2019. "Wind Turbine Power Curve Upgrades: Part II," Energies, MDPI, vol. 12(8), pages 1-20, April.
    9. Gionfra, Nicolò & Sandou, Guillaume & Siguerdidjane, Houria & Faille, Damien & Loevenbruck, Philippe, 2019. "Wind farm distributed PSO-based control for constrained power generation maximization," Renewable Energy, Elsevier, vol. 133(C), pages 103-117.
    10. Wu, Yan & Xia, Tianqi & Wang, Yufei & Zhang, Haoran & Feng, Xiao & Song, Xuan & Shibasaki, Ryosuke, 2022. "A synchronization methodology for 3D offshore wind farm layout optimization with multi-type wind turbines and obstacle-avoiding cable network," Renewable Energy, Elsevier, vol. 185(C), pages 302-320.
    11. Lo Brutto, Ottavio A. & Thiébot, Jérôme & Guillou, Sylvain S. & Gualous, Hamid, 2016. "A semi-analytic method to optimize tidal farm layouts – Application to the Alderney Race (Raz Blanchard), France," Applied Energy, Elsevier, vol. 183(C), pages 1168-1180.
    12. Lo Brutto, Ottavio A. & Guillou, Sylvain S. & Thiébot, Jérôme & Gualous, Hamid, 2017. "Assessing the effectiveness of a global optimum strategy within a tidal farm for power maximization," Applied Energy, Elsevier, vol. 204(C), pages 653-666.
    13. Dinçer, A.E. & Demir, A. & Yılmaz, K., 2024. "Multi-objective turbine allocation on a wind farm site," Applied Energy, Elsevier, vol. 355(C).
    14. Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2018. "Continuous adjoint formulation for wind farm layout optimization: A 2D implementation," Applied Energy, Elsevier, vol. 228(C), pages 2333-2345.
    15. Yin, Xiuxing & Zhao, Xiaowei & Lin, Jin & Karcanias, Aris, 2020. "Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations," Energy, Elsevier, vol. 202(C).
    16. Zhao, Xin-gang & Ren, Ling-zhi, 2015. "Focus on the development of offshore wind power in China: Has the golden period come?," Renewable Energy, Elsevier, vol. 81(C), pages 644-657.
    17. Yang, Kyoungboo & Kwak, Gyeongil & Cho, Kyungho & Huh, Jongchul, 2019. "Wind farm layout optimization for wake effect uniformity," Energy, Elsevier, vol. 183(C), pages 983-995.
    18. Van-Hai Bui & Akhtar Hussain & Woon-Gyu Lee & Hak-Man Kim, 2019. "Multi-Objective Optimization for Determining Trade-Off between Output Power and Power Fluctuations in Wind Farm System," Energies, MDPI, vol. 12(22), pages 1-18, November.
    19. Croonenbroeck, Carsten & Hennecke, David, 2021. "A comparison of optimizers in a unified standard for optimization on wind farm layout optimization," Energy, Elsevier, vol. 216(C).
    20. Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2020. "Optimal design of wind farms in complex terrains using computational fluid dynamics and adjoint methods," Applied Energy, Elsevier, vol. 261(C).

    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:12:y:2019:i:5:p:863-:d:211103. 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.