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Fast Control-Oriented Dynamic Linear Model of Wind Farm Flow and Operation

Author

Listed:
  • Jonas Kazda

    (Wind Energy, Technical University of Denmark, 4000 Roskilde, Denmark)

  • Nicolaos Antonio Cutululis

    (Wind Energy, Technical University of Denmark, 4000 Roskilde, Denmark)

Abstract

The aerodynamic interaction between wind turbines grouped in wind farms results in wake-induced power loss and fatigue loads of wind turbines. To mitigate these, wind farm control should be able to account for those interactions, typically using model-based approaches. Such model-based control approaches benefit from computationally fast, linear models and therefore, in this work, we introduce the Dynamic Flow Predictor. It is a fast, control-oriented, dynamic, linear model of wind farm flow and operation that provides predictions of wind speed and turbine power. The model estimates wind turbine aerodynamic interaction using a linearized engineering wake model in combination with a delay process. The Dynamic Flow Predictor was tested on a two-turbine array to illustrate its main characteristics and on a large-scale wind farm, comparable to modern offshore wind farms, to illustrate its scalability and accuracy in a more realistic scale. The simulations were performed in SimWindFarm with wind turbines represented using the NREL 5 MW model. The results showed the suitability, accuracy, and computational speed of the modeling approach. In the study on the large-scale wind farm, rotor effective wind speed was estimated with a root-mean-square error ranging between 0.8% and 4.1%. In the same study, the computation time per iteration of the model was, on average, 2.1 × 10 − 5 s. It is therefore concluded that the presented modeling approach is well suited for use in wind farm control.

Suggested Citation

  • Jonas Kazda & Nicolaos Antonio Cutululis, 2018. "Fast Control-Oriented Dynamic Linear Model of Wind Farm Flow and Operation," Energies, MDPI, vol. 11(12), pages 1-19, November.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3346-:d:186786
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    References listed on IDEAS

    as
    1. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    2. Hansen, Anca D. & Sørensen, Poul & Iov, Florin & Blaabjerg, Frede, 2006. "Centralised power control of wind farm with doubly fed induction generators," Renewable Energy, Elsevier, vol. 31(7), pages 935-951.
    3. Unai Fernandez-Gamiz & Ekaitz Zulueta & Ana Boyano & Igor Ansoategui & Irantzu Uriarte, 2017. "Five Megawatt Wind Turbine Power Output Improvements by Passive Flow Control Devices," Energies, MDPI, vol. 10(6), pages 1-15, May.
    4. Wang, Jianzhou & Hu, Jianming, 2015. "A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vec," Energy, Elsevier, vol. 93(P1), pages 41-56.
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    Cited by:

    1. Yingming Liu & Yingwei Wang & Xiaodong Wang & Jiangsheng Zhu & Wai Hou Lio, 2019. "Active Power Dispatch for Supporting Grid Frequency Regulation in Wind Farms Considering Fatigue Load," Energies, MDPI, vol. 12(8), pages 1-23, April.

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