IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v87y2010i10p3283-3293.html
   My bibliography  Save this article

Assessing transient response of DFIG based wind turbines during voltage dips regarding main flux saturation and rotor deep-bar effect

Author

Listed:
  • Song, Zhanfeng
  • Xia, Changliang
  • Shi, Tingna

Abstract

With increasing wind power penetration, transient responses of doubly-fed-induction-generator (DFIG) based wind turbines gain attentive focus. Accurate prediction of transient performance of DFIG under grid faults is required with increasing wind power penetration. Taking into account the main flux saturation and deep-bar effect, this paper concentrates on transient responses and stability of the DFIG system under symmetrical grid faults. Their roles played in the enhancement of system transient stability are clarified. The analyses proposed contribute greatly to proper selection, design and coordination of protection devices and control strategies as well as stability studies.

Suggested Citation

  • Song, Zhanfeng & Xia, Changliang & Shi, Tingna, 2010. "Assessing transient response of DFIG based wind turbines during voltage dips regarding main flux saturation and rotor deep-bar effect," Applied Energy, Elsevier, vol. 87(10), pages 3283-3293, October.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:10:p:3283-3293
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306-2619(10)00116-9
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Brahmi, Jemaa & Krichen, Lotfi & Ouali, Abderrazak, 2009. "A comparative study between three sensorless control strategies for PMSG in wind energy conversion system," Applied Energy, Elsevier, vol. 86(9), pages 1565-1573, September.
    2. Ahshan, R. & Iqbal, M.T. & Mann, George K.I., 2008. "Controller for a small induction-generator based wind-turbine," Applied Energy, Elsevier, vol. 85(4), pages 218-227, April.
    3. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    4. Hansen, Anca D. & Michalke, Gabriele, 2007. "Fault ride-through capability of DFIG wind turbines," Renewable Energy, Elsevier, vol. 32(9), pages 1594-1610.
    5. García-Gracia, Miguel & Paz Comech, M. & Sallán, Jesús & López-Andía, Diego & Alonso, Oscar, 2009. "Voltage dip generator for wind energy systems up to 5Â MW," Applied Energy, Elsevier, vol. 86(4), pages 565-574, April.
    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. Chi-Jeng Bai & Wei-Cheng Wang & Po-Wei Chen & Wen-Tong Chong, 2014. "System Integration of the Horizontal-Axis Wind Turbine: The Design of Turbine Blades with an Axial-Flux Permanent Magnet Generator," Energies, MDPI, vol. 7(11), pages 1-21, November.
    2. Ochoa, Danny & Martinez, Sergio, 2018. "Frequency dependent strategy for mitigating wind power fluctuations of a doubly-fed induction generator wind turbine based on virtual inertia control and blade pitch angle regulation," Renewable Energy, Elsevier, vol. 128(PA), pages 108-124.
    3. Zhou, Yu & Li, Zhengshuo & Wang, Guangrui, 2021. "Study on leveraging wind farms' robust reactive power range for uncertain power system reactive power optimization," Applied Energy, Elsevier, vol. 298(C).
    4. Xiao, Zhao-xia & Guerrero, Josep M. & Shuang, Jia & Sera, Dezso & Schaltz, Erik & Vásquez, Juan C., 2018. "Flat tie-line power scheduling control of grid-connected hybrid microgrids," Applied Energy, Elsevier, vol. 210(C), pages 786-799.
    5. Jia, Ke & Li, Yanbin & Fang, Yu & Zheng, Liming & Bi, Tianshu & Yang, Qixun, 2018. "Transient current similarity based protection for wind farm transmission lines," Applied Energy, Elsevier, vol. 225(C), pages 42-51.
    6. Xia, S.W. & Bu, S.Q. & Zhang, X. & Xu, Y. & Zhou, B. & Zhu, J.B., 2018. "Model reduction strategy of doubly-fed induction generator-based wind farms for power system small-signal rotor angle stability analysis," Applied Energy, Elsevier, vol. 222(C), pages 608-620.
    7. Ayodele, T.R. & Ogunjuyigbe, A.S.O. & Adetokun, B.B., 2017. "Optimal capacitance selection for a wind-driven self-excited reluctance generator under varying wind speed and load conditions," Applied Energy, Elsevier, vol. 190(C), pages 339-353.
    8. Yao, Jun & Liu, Ruikuo & Zhou, Te & Hu, Weihao & Chen, Zhe, 2017. "Coordinated control strategy for hybrid wind farms with DFIG-based and PMSG-based wind farms during network unbalance," Renewable Energy, Elsevier, vol. 105(C), pages 748-763.
    9. Song, Zhanfeng & Shi, Tingna & Xia, Changliang & Chen, Wei, 2012. "A novel adaptive control scheme for dynamic performance improvement of DFIG-Based wind turbines," Energy, Elsevier, vol. 38(1), pages 104-117.
    10. Díaz-González, Francisco & Sumper, Andreas & Gomis-Bellmunt, Oriol & Bianchi, Fernando D., 2013. "Energy management of flywheel-based energy storage device for wind power smoothing," Applied Energy, Elsevier, vol. 110(C), pages 207-219.
    11. Modi, Nilesh & Saha, Tapan K. & Anderson, Tom, 2013. "Damping performance of the large scale Queensland transmission network with significant wind penetration," Applied Energy, Elsevier, vol. 111(C), pages 225-233.
    12. Yan Yan & Meng Wang & Zhan-Feng Song & Chang-Liang Xia, 2012. "Proportional-Resonant Control of Doubly-Fed Induction Generator Wind Turbines for Low-Voltage Ride-Through Enhancement," Energies, MDPI, vol. 5(11), pages 1-21, November.
    13. Boynuegri, A.R. & Vural, B. & Tascikaraoglu, A. & Uzunoglu, M. & Yumurtacı, R., 2012. "Voltage regulation capability of a prototype Static VAr Compensator for wind applications," Applied Energy, Elsevier, vol. 93(C), pages 422-431.
    14. Belmokhtar, K. & Doumbia, M.L. & Agbossou, K., 2014. "Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator)," Energy, Elsevier, vol. 76(C), pages 679-693.

    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. Kalantar, M. & Mousavi G., S.M., 2010. "Dynamic behavior of a stand-alone hybrid power generation system of wind turbine, microturbine, solar array and battery storage," Applied Energy, Elsevier, vol. 87(10), pages 3051-3064, October.
    2. Constantina Kopitsa & Ioannis G. Tsoulos & Vasileios Charilogis & Athanassios Stavrakoudis, 2024. "Predicting the Duration of Forest Fires Using Machine Learning Methods," Future Internet, MDPI, vol. 16(11), pages 1-19, October.
    3. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    4. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
    5. Yıldıran, Uğur & Kayahan, İsmail, 2018. "Risk-averse stochastic model predictive control-based real-time operation method for a wind energy generation system supported by a pumped hydro storage unit," Applied Energy, Elsevier, vol. 226(C), pages 631-643.
    6. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    7. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    8. Sen Guo & Haoran Zhao & Huiru Zhao, 2017. "A New Hybrid Wind Power Forecaster Using the Beveridge-Nelson Decomposition Method and a Relevance Vector Machine Optimized by the Ant Lion Optimizer," Energies, MDPI, vol. 10(7), pages 1-20, July.
    9. Dixon, Christopher & Reynolds, Steve & Rodley, David, 2016. "Micro/small wind turbine power control for electrolysis applications," Renewable Energy, Elsevier, vol. 87(P1), pages 182-192.
    10. Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
    11. Justo, Jackson John & Mwasilu, Francis & Jung, Jin-Woo, 2015. "Doubly-fed induction generator based wind turbines: A comprehensive review of fault ride-through strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 447-467.
    12. Hannah Jessie Rani R. & Aruldoss Albert Victoire T., 2018. "Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-35, May.
    13. Niu, Tong & Wang, Jianzhou & Zhang, Kequan & Du, Pei, 2018. "Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy," Renewable Energy, Elsevier, vol. 118(C), pages 213-229.
    14. Alphonsus, Ephrem Ryan & Abdullah, Mohammad Omar, 2016. "A review on the applications of programmable logic controllers (PLCs)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1185-1205.
    15. Ruiz de la Hermosa González-Carrato, Raúl & García Márquez, Fausto Pedro & Dimlaye, Vichaar, 2015. "Maintenance management of wind turbines structures via MFCs and wavelet transforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 472-482.
    16. Eissa (SIEEE), M.M., 2015. "Protection techniques with renewable resources and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1645-1667.
    17. Daniela Castro-Camilo & Raphaël Huser & Håvard Rue, 2019. "A Spliced Gamma-Generalized Pareto Model for Short-Term Extreme Wind Speed Probabilistic Forecasting," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 517-534, September.
    18. Burlibaşa, A. & Ceangă, E., 2013. "Rotationally sampled spectrum approach for simulation of wind speed turbulence in large wind turbines," Applied Energy, Elsevier, vol. 111(C), pages 624-635.
    19. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.
    20. Nadire Cavus & Yakubu Bala Mohammed & Mohammed Nasiru Yakubu, 2021. "An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps," Sustainability, MDPI, vol. 13(11), pages 1-21, May.

    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:appene:v:87:y:2010:i:10:p:3283-3293. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.