IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v190y2017icp670-685.html
   My bibliography  Save this article

Wind estimation with a non-standard extended Kalman filter and its application on maximum power extraction for variable speed wind turbines

Author

Listed:
  • Song, Dongran
  • Yang, Jian
  • Cai, Zili
  • Dong, Mi
  • Su, Mei
  • Wang, Yinghua

Abstract

To maximize power extraction at below-rated wind speeds, variable-speed wind turbines must be controlled by tracking the optimal TSR (tip speed ratio) and pitch angle, which depend on the wind speed measured by nacelle anemometers or provided by an EWS (effective wind speed) estimator. However, the measured values are imprecise and existing estimators cannot provide qualified estimates. This paper addresses this problem by presenting a novel solution with a non-standard extended Kalman filter. To avoid using imprecise wind speed measurements or other costly measurement devices, the proposed solution employs a virtual measurement that is calculated from related estimated states. In addition, the solution presents an internal EWS model by considering the tower shadow effect, so the obtained model is more general than the statistical model that is difficult to obtain in practice. Compared with existing estimators, the proposed estimator provides more precise estimated results and is suitable for control application. Its application is investigated on the MPE (maximum power extraction) of a variable speed wind turbine, for which an industrial baseline controller is optimized by enhancing the optimal TSR tracking and pitch adjustment. The proposed solutions are validated using both simulation and field testing results. Comparing the proposed estimation solution to two existing methods demonstrates that the former gives the best estimate results. Moreover, its application for the MPE increases annual energy production by approximately 0.8% in comparison with the baseline controller, which is a considerable energy production increment.

Suggested Citation

  • Song, Dongran & Yang, Jian & Cai, Zili & Dong, Mi & Su, Mei & Wang, Yinghua, 2017. "Wind estimation with a non-standard extended Kalman filter and its application on maximum power extraction for variable speed wind turbines," Applied Energy, Elsevier, vol. 190(C), pages 670-685.
    • Handle: RePEc:eee:appene:v:190:y:2017:i:c:p:670-685
    DOI: 10.1016/j.apenergy.2016.12.132
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916319158
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.12.132?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. Jena, Debashisha & Rajendran, Saravanakumar, 2015. "A review of estimation of effective wind speed based control of wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1046-1062.
    2. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
    3. Beccali, M. & Cirrincione, G. & Marvuglia, A. & Serporta, C., 2010. "Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor," Applied Energy, Elsevier, vol. 87(3), pages 884-893, March.
    4. Pagnini, Luisa C. & Burlando, Massimiliano & Repetto, Maria Pia, 2015. "Experimental power curve of small-size wind turbines in turbulent urban environment," Applied Energy, Elsevier, vol. 154(C), pages 112-121.
    5. Mohandes, M. & Rehman, S. & Rahman, S.M., 2011. "Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)," Applied Energy, Elsevier, vol. 88(11), pages 4024-4032.
    6. 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.
    7. Seyed Mojtaba Tabatabaeipour & Peter F. Odgaard & Thomas Bak & Jakob Stoustrup, 2012. "Fault Detection of Wind Turbines with Uncertain Parameters: A Set-Membership Approach," Energies, MDPI, vol. 5(7), pages 1-25, July.
    8. Fathabadi, Hassan, 2016. "Novel high-efficient unified maximum power point tracking controller for hybrid fuel cell/wind systems," Applied Energy, Elsevier, vol. 183(C), pages 1498-1510.
    9. Chehouri, Adam & Younes, Rafic & Ilinca, Adrian & Perron, Jean, 2015. "Review of performance optimization techniques applied to wind turbines," Applied Energy, Elsevier, vol. 142(C), pages 361-388.
    10. Ganjefar, Soheil & Mohammadi, Ali, 2016. "Variable speed wind turbines with maximum power extraction using singular perturbation theory," Energy, Elsevier, vol. 106(C), pages 510-519.
    11. Yang, Jian & Song, Dongran & Dong, Mi & Chen, Sifan & Zou, Libing & Guerrero, Josep M., 2016. "Comparative studies on control systems for a two-blade variable-speed wind turbine with a speed exclusion zone," Energy, Elsevier, vol. 109(C), pages 294-309.
    12. Zuluaga, Carlos D. & Álvarez, Mauricio A. & Giraldo, Eduardo, 2015. "Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison," Applied Energy, Elsevier, vol. 156(C), pages 321-330.
    13. Shu, Z.R. & Li, Q.S. & He, Y.C. & Chan, P.W., 2016. "Observations of offshore wind characteristics by Doppler-LiDAR for wind energy applications," Applied Energy, Elsevier, vol. 169(C), pages 150-163.
    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. Song, Dongran & Yang, Jian & Su, Mei & Liu, Anfeng & Cai, Zili & Liu, Yao & Joo, Young Hoon, 2017. "A novel wind speed estimator-integrated pitch control method for wind turbines with global-power regulation," Energy, Elsevier, vol. 138(C), pages 816-830.
    2. Dongran Song & Jian Yang & Mei Su & Anfeng Liu & Yao Liu & Young Hoon Joo, 2017. "A Comparison Study between Two MPPT Control Methods for a Large Variable-Speed Wind Turbine under Different Wind Speed Characteristics," Energies, MDPI, vol. 10(5), pages 1-18, May.
    3. Song, Dongran & Yang, Jian & Dong, Mi & Joo, Young Hoon, 2017. "Model predictive control with finite control set for variable-speed wind turbines," Energy, Elsevier, vol. 126(C), pages 564-572.
    4. Song, Dongran & Fan, Xinyu & Yang, Jian & Liu, Anfeng & Chen, Sifan & Joo, Young Hoon, 2018. "Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method," Applied Energy, Elsevier, vol. 224(C), pages 267-279.
    5. Jin, Yuqing & Ju, Ping & Rehtanz, Christian & Wu, Feng & Pan, Xueping, 2018. "Equivalent modeling of wind energy conversion considering overall effect of pitch angle controllers in wind farm," Applied Energy, Elsevier, vol. 222(C), pages 485-496.
    6. Bizon, Nicu, 2018. "Optimal operation of fuel cell/wind turbine hybrid power system under turbulent wind and variable load," Applied Energy, Elsevier, vol. 212(C), pages 196-209.
    7. 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.
    8. 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.
    9. Liu, Xiong & Lu, Cheng & Liang, Shi & Godbole, Ajit & Chen, Yan, 2017. "Vibration-induced aerodynamic loads on large horizontal axis wind turbine blades," Applied Energy, Elsevier, vol. 185(P2), pages 1109-1119.
    10. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
    11. Youssef, Abdel-Raheem & Mousa, Hossam H.H. & Mohamed, Essam E.M., 2020. "Development of self-adaptive P&O MPPT algorithm for wind generation systems with concentrated search area," Renewable Energy, Elsevier, vol. 154(C), pages 875-893.
    12. Wang, Yun & Wang, Haibo & Srinivasan, Dipti & Hu, Qinghua, 2019. "Robust functional regression for wind speed forecasting based on Sparse Bayesian learning," Renewable Energy, Elsevier, vol. 132(C), pages 43-60.
    13. Fathabadi, Hassan, 2016. "Novel high-efficient unified maximum power point tracking controller for hybrid fuel cell/wind systems," Applied Energy, Elsevier, vol. 183(C), pages 1498-1510.
    14. 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.
    15. Hamed Habibi & Hamed Rahimi Nohooji & Ian Howard & Silvio Simani, 2019. "Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation," Energies, MDPI, vol. 12(24), pages 1-33, December.
    16. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    17. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    18. Abrar Ahmed Chhipa & Vinod Kumar & Raghuveer Raj Joshi & Prasun Chakrabarti & Michal Jasinski & Alessandro Burgio & Zbigniew Leonowicz & Elzbieta Jasinska & Rajkumar Soni & Tulika Chakrabarti, 2021. "Adaptive Neuro-Fuzzy Inference System-Based Maximum Power Tracking Controller for Variable Speed WECS," Energies, MDPI, vol. 14(19), pages 1-19, October.
    19. Veisi, Amin Allah & Shafiei Mayam, Mohammad Hossein, 2017. "Effects of blade rotation direction in the wake region of two in-line turbines using Large Eddy Simulation," Applied Energy, Elsevier, vol. 197(C), pages 375-392.
    20. Assareh, Ehsanolah & Biglari, Mojtaba, 2015. "A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1023-1037.

    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:190:y:2017:i:c:p:670-685. 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.