IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v178y2021icp1129-1143.html
   My bibliography  Save this article

Generic EMT modeling method of Type-4 wind turbine generators based on detailed FRT studies

Author

Listed:
  • Qi, Jinling
  • Li, Weixing
  • Chao, Pupu
  • Liang, Xiaodong
  • Sun, Yong
  • Li, Zhimin

Abstract

Developing effective electromagnetic transient (EMT) models of wind turbine generators (WTGs) is essential for power system transient analysis. However, manufacturer-specific EMT models are usually available as black boxes, which is not easy to be used in practical applications. To address this issue, a four-step generic EMT modeling method is proposed for type-4 WTGs based on detailed fault ride-through (FRT) studies. Step one, massive FRT field tests are conducted on type-4 WTGs, and generic FRT responses of active and reactive power are extracted and parameterized to characterize a wide variety of controller strategies of various vendors; Step two, piecewise expressions of active and reactive power responses are derived to formulate the detailed FRT processes, including the FRT control process during a fault, post-fault recovery control process, and their switching transients; Step three, a generic controller, to emulate the FRT responses, is implemented in the WTG model by automatically updating current references utilizing the derived analytical formulation; Step four, a stepwise identification method is proposed to identify the FRT control parameters, proportional-integral controller parameters, and circuit parameters. The proposed modeling method is verified by field tests, and the results show that the developed model can accurately capture the FRT behaviors of practical type-4 WTGs.

Suggested Citation

  • Qi, Jinling & Li, Weixing & Chao, Pupu & Liang, Xiaodong & Sun, Yong & Li, Zhimin, 2021. "Generic EMT modeling method of Type-4 wind turbine generators based on detailed FRT studies," Renewable Energy, Elsevier, vol. 178(C), pages 1129-1143.
    • Handle: RePEc:eee:renene:v:178:y:2021:i:c:p:1129-1143
    DOI: 10.1016/j.renene.2021.06.057
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014812100923X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2021.06.057?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nasiri, M. & Milimonfared, J. & Fathi, S.H., 2015. "A review of low-voltage ride-through enhancement methods for permanent magnet synchronous generator based wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 399-415.
    2. Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.
    3. Tripathi, S.M. & Tiwari, A.N. & Singh, Deependra, 2015. "Grid-integrated permanent magnet synchronous generator based wind energy conversion systems: A technology review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1288-1305.
    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. Manisha Sawant & Sameer Thakare & A. Prabhakara Rao & Andrés E. Feijóo-Lorenzo & Neeraj Dhanraj Bokde, 2021. "A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics," Energies, MDPI, vol. 14(8), pages 1-30, April.
    2. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    3. Mojtaba Nasiri & Saleh Mobayen & Quan Min Zhu, 2019. "Super-Twisting Sliding Mode Control for Gearless PMSG-Based Wind Turbine," Complexity, Hindawi, vol. 2019, pages 1-15, April.
    4. Brooks, Sam & Mahmood, Minhal & Roy, Rajkumar & Manolesos, Marinos & Salonitis, Konstantinos, 2023. "Self-reconfiguration simulations of turbines to reduce uneven farm degradation," Renewable Energy, Elsevier, vol. 206(C), pages 1301-1314.
    5. Shijia Zhou & Fei Rong & Zhangtao Yin & Shoudao Huang & Yuebin Zhou, 2018. "HVDC Transmission Technology of Wind Power System with Multi-Phase PMSG," Energies, MDPI, vol. 11(12), pages 1-16, November.
    6. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    7. Zhicheng Lin & Song Zheng & Zhicheng Chen & Rong Zheng & Wang Zhang, 2019. "Application Research of the Parallel System Theory and the Data Engine Approach in Wind Energy Conversion System," Energies, MDPI, vol. 12(5), pages 1-20, March.
    8. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).
    9. Lasantha Meegahapola & Alfeu Sguarezi & Jack Stanley Bryant & Mingchen Gu & Eliomar R. Conde D. & Rafael B. A. Cunha, 2020. "Power System Stability with Power-Electronic Converter Interfaced Renewable Power Generation: Present Issues and Future Trends," Energies, MDPI, vol. 13(13), pages 1-35, July.
    10. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    11. Narayana, Mahinsasa & Sunderland, Keith M. & Putrus, Ghanim & Conlon, Michael F., 2017. "Adaptive linear prediction for optimal control of wind turbines," Renewable Energy, Elsevier, vol. 113(C), pages 895-906.
    12. Yang, Zhimin & Chai, Yi, 2016. "A survey of fault diagnosis for onshore grid-connected converter in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 345-359.
    13. Wajahat Ullah Khan Tareen & Muhammad Aamir & Saad Mekhilef & Mutsuo Nakaoka & Mehdi Seyedmahmoudian & Ben Horan & Mudasir Ahmed Memon & Nauman Anwar Baig, 2018. "Mitigation of Power Quality Issues Due to High Penetration of Renewable Energy Sources in Electric Grid Systems Using Three-Phase APF/STATCOM Technologies: A Review," Energies, MDPI, vol. 11(6), pages 1-41, June.
    14. Xiangwu Yan & Linlin Yang & Tiecheng Li, 2021. "The LVRT Control Scheme for PMSG-Based Wind Turbine Generator Based on the Coordinated Control of Rotor Overspeed and Supercapacitor Energy Storage," Energies, MDPI, vol. 14(2), pages 1-22, January.
    15. Vasudevan, Krishnakumar R. & Ramachandaramurthy, Vigna K. & Venugopal, Gomathi & Ekanayake, J.B. & Tiong, S.K., 2021. "Variable speed pumped hydro storage: A review of converters, controls and energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    16. Xia, Tangbin & Dong, Yifan & Pan, Ershun & Zheng, Meimei & Wang, Hao & Xi, Lifeng, 2021. "Fleet-level opportunistic maintenance for large-scale wind farms integrating real-time prognostic updating," Renewable Energy, Elsevier, vol. 163(C), pages 1444-1454.
    17. Barra, P.H.A. & de Carvalho, W.C. & Menezes, T.S. & Fernandes, R.A.S. & Coury, D.V., 2021. "A review on wind power smoothing using high-power energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    18. Do, M. Hung & Söffker, Dirk, 2021. "State-of-the-art in integrated prognostics and health management control for utility-scale wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    19. Kong, Yun & Qin, Zhaoye & Wang, Tianyang & Han, Qinkai & Chu, Fulei, 2021. "An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines," Renewable Energy, Elsevier, vol. 173(C), pages 987-1004.
    20. Shrabani Sahu & Sasmita Behera, 2022. "A review on modern control applications in wind energy conversion system," Energy & Environment, , vol. 33(2), pages 223-262, March.

    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:eee:renene:v:178:y:2021:i:c:p:1129-1143. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.