IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i5p4220-d1081197.html

Environmental Condition Boundary Design for Direct-Drive Permanent Magnet (DDPM) Wind Generators by Using Extreme Joint Probability Distribution

Author

Listed:
  • De Tian

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Jing Xia

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
    Beijing Goldwind Science & Creation Windpower Equipment Co., Ltd., Beijing 100176, China)

  • Xiaoya Liu

    (Beijing Goldwind Science & Creation Windpower Equipment Co., Ltd., Beijing 100176, China)

  • Jingjing Hao

    (Beijing Goldwind Science & Creation Windpower Equipment Co., Ltd., Beijing 100176, China)

  • Yan Li

    (Beijing Goldwind Science & Creation Windpower Equipment Co., Ltd., Beijing 100176, China)

  • Peng Li

    (School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China)

Abstract

In future engineering applications, it is important for a direct-drive permanent magnet (DDPM) wind generator to be designed with optimized environmental condition boundary. This paper presents a novel extreme joint probability distribution method of boundary design to formulate the evaluation model and correlation between component design and environmental conditions. With this method, the joint probability distributions of multidimensional parameters for typical wind resource areas in China are studied. A 3.3-MW DDPM wind generator is involved in the case study to validate the superiority of the method. Furthermore, to improve the generalizability of the method, some typical wind resource data platforms are calibrated regarding the measured data. It is shown that the ERA5 dataset can be used as a supplement to enhance the representativeness of the measured data for the joint probability distributions. Therefore, the proposed method can be potentially used to optimize the system design of future DDPM wind generators.

Suggested Citation

  • De Tian & Jing Xia & Xiaoya Liu & Jingjing Hao & Yan Li & Peng Li, 2023. "Environmental Condition Boundary Design for Direct-Drive Permanent Magnet (DDPM) Wind Generators by Using Extreme Joint Probability Distribution," Sustainability, MDPI, vol. 15(5), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4220-:d:1081197
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/5/4220/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/5/4220/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Toft, Henrik Stensgaard & Svenningsen, Lasse & Sørensen, John Dalsgaard & Moser, Wolfgang & Thøgersen, Morten Lybech, 2016. "Uncertainty in wind climate parameters and their influence on wind turbine fatigue loads," Renewable Energy, Elsevier, vol. 90(C), pages 352-361.
    2. Chen, Jincheng & Wang, Feng & Stelson, Kim A., 2018. "A mathematical approach to minimizing the cost of energy for large utility wind turbines," Applied Energy, Elsevier, vol. 228(C), pages 1413-1422.
    3. Shields, Matt & Beiter, Philipp & Nunemaker, Jake & Cooperman, Aubryn & Duffy, Patrick, 2021. "Impacts of turbine and plant upsizing on the levelized cost of energy for offshore wind," Applied Energy, Elsevier, vol. 298(C).
    4. Fabian Vorpahl & Holger Schwarze & Tim Fischer & Marc Seidel & Jason Jonkman, 2013. "Offshore wind turbine environment, loads, simulation, and design," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 2(5), pages 548-570, September.
    Full references (including those not matched with items on IDEAS)

    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. Marzouk, Osama A., 2025. "Wind Speed Weibull Model Identification in Oman, and Computed Normalized Annual Energy Production (NAEP) From Wind Turbines Based on Data From Weather Stations," OSF Preprints 8jvcn_v1, Center for Open Science.
    2. Velarde, Joey & Kramhøft, Claus & Sørensen, John Dalsgaard, 2019. "Global sensitivity analysis of offshore wind turbine foundation fatigue loads," Renewable Energy, Elsevier, vol. 140(C), pages 177-189.
    3. Liang Lu & Minyan Zhu & Haijun Wu & Jianzhong Wu, 2022. "A Review and Case Analysis on Biaxial Synchronous Loading Technology and Fast Moment-Matching Methods for Fatigue Tests of Wind Turbine Blades," Energies, MDPI, vol. 15(13), pages 1-34, July.
    4. 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.
    5. Farid Khazaeli Moghadam & Nils Desch, 2023. "Life Cycle Assessment of Various PMSG-Based Drivetrain Concepts for 15 MW Offshore Wind Turbines Applications," Energies, MDPI, vol. 16(3), pages 1-26, February.
    6. Bozzoli, Leonardo & Cipolletta, Mariasole & Dal Pozzo, Alessandro & Nucci, Carlo Alberto & Cozzani, Valerio, 2025. "Assessing the environmental sustainability of alternative design concepts for transmission systems of offshore renewable energy," Applied Energy, Elsevier, vol. 399(C).
    7. Kresning, Boma & Hashemi, M. Reza & Shirvani, Amin & Hashemi, Javad, 2024. "Uncertainty of extreme wind and wave loads for marine renewable energy farms in hurricane-prone regions," Renewable Energy, Elsevier, vol. 220(C).
    8. Shields, Matt & Beiter, Philipp & Nunemaker, Jake & Cooperman, Aubryn & Duffy, Patrick, 2021. "Impacts of turbine and plant upsizing on the levelized cost of energy for offshore wind," Applied Energy, Elsevier, vol. 298(C).
    9. Zhang, Lijun & Li, Ye & Xu, Wenhao & Gao, Zhiteng & Fang, Long & Li, Rongfu & Ding, Boyin & Zhao, Bin & Leng, Jun & He, Fenglan, 2022. "Systematic analysis of performance and cost of two floating offshore wind turbines with significant interactions," Applied Energy, Elsevier, vol. 321(C).
    10. Regodeseves, P. García & Morros, C. Santolaria, 2026. "Interaction between flow field and blade forces on HAWT integrating Lagrangian interpolation of velocities with an actuator volume technique," Renewable Energy, Elsevier, vol. 256(PA).
    11. Ma, Yuxiang & Zhao, Rubo & Zhao, Wenhua & Tai, Bing & Dong, Guohai, 2025. "Dynamic analysis to reduce the cost for fixed offshore wind energy turbines," Applied Energy, Elsevier, vol. 378(PA).
    12. Jeong, Michael & Loth, Eric & Qin, Chris & Selig, Michael & Johnson, Nick, 2024. "Aerodynamic rotor design for a 25 MW offshore downwind turbine," Applied Energy, Elsevier, vol. 353(PA).
    13. Lin, Hai & Wu, Yan & Cui, Ziyuan & Yang, Sen & Yu, Xinhai & Wang, Yufei, 2025. "An integrated optimization method for distributed wind farm design considering three-dimensional complex terrain," Renewable Energy, Elsevier, vol. 255(C).
    14. Yang Ni & Bin Peng & Jiayao Wang & Farshad Golnary & Wei Li, 2023. "A Short Review on the Time-Domain Numerical Simulations for Structural Responses in Horizontal-Axis Offshore Wind Turbines," Sustainability, MDPI, vol. 15(24), pages 1-19, December.
    15. 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).
    16. Papi, F. & Bianchini, A., 2022. "Technical challenges in floating offshore wind turbine upscaling: A critical analysis based on the NREL 5 MW and IEA 15 MW Reference Turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    17. Weiyi Jiang & Taeyong Jung & Hancheng Dai & Pianpian Xiang & Sha Chen, 2025. "Transition Pathways for Low-Carbon Steel Manufacture in East Asia: The Role of Renewable Energy and Technological Collaboration," Sustainability, MDPI, vol. 17(10), pages 1-14, May.
    18. Hu, Weicheng & Yang, Qingshan & Yuan, Ziting & Yang, Fucheng, 2024. "Wind farm layout optimization in complex terrain based on CFD and IGA-PSO," Energy, Elsevier, vol. 288(C).
    19. Lee, Namkyoung & Lee, Hyuntae & Joung, Seulgi, 2025. "A wake-induced two-phase planning framework for offshore wind farm maintenance with stochastic mixed-integer program," Applied Energy, Elsevier, vol. 380(C).
    20. Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:15:y:2023:i:5:p:4220-:d:1081197. 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.