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Bilateral Gaussian Wake Model Formulation for Wind Farms: A Forecasting based approach

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  • Dhiman, Harsh S.
  • Deb, Dipankar
  • Foley, Aoife M.

Abstract

Optimal placement of turbines in a wind farm is a major challenge where the wake effect reduces the effective wind power capture. Wind speed prediction is essential from a reliability point of view. In this article, a bilateral wake model which is derived from two benchmark models, namely, Jensen's and Frandsen's variation is used for studying the performance of far-end wakes. A prediction based approach is formulated wherein the inputs to the classical SVR model are based on the two benchmark models and the proposed bilateral Gaussian wake model. Wind speed is predicted for upstream turbines of two wind farm layouts (5-turbine and 15-turbine). Further, to observe the impact of input dimensionality, two techniques: (i) Grey relational analysis (GRA) and (ii) Neighborhood component analysis (NCA), are considered. Results reveal that for a wind site WBZ tower, NCA outperforms GRA by 36.48%, 34.0% and 7.03% for Jensen's, Frandsen's and bilateral wake model respectively. When compared to the two benchmark models for both the techniques (GRA and NCA), the prediction performance of bilateral wake model is superior. Overall, it is observed that the feature selection tools like GRA and NCA improve the wind speed prediction accuracy in the presence of wind wakes.

Suggested Citation

  • Dhiman, Harsh S. & Deb, Dipankar & Foley, Aoife M., 2020. "Bilateral Gaussian Wake Model Formulation for Wind Farms: A Forecasting based approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    • Handle: RePEc:eee:rensus:v:127:y:2020:i:c:s1364032120301660
    DOI: 10.1016/j.rser.2020.109873
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    Cited by:

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    4. Purohit, Shantanu & Ng, E.Y.K. & Syed Ahmed Kabir, Ijaz Fazil, 2022. "Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake," Renewable Energy, Elsevier, vol. 184(C), pages 405-420.
    5. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Li, Zhuo, 2021. "Feature extraction of meteorological factors for wind power prediction based on variable weight combined method," Renewable Energy, Elsevier, vol. 179(C), pages 1925-1939.
    6. Kaldellis, John K. & Triantafyllou, Panagiotis & Stinis, Panagiotis, 2021. "Critical evaluation of Wind Turbines’ analytical wake models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    7. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    8. Emilio Porcu & Jonas Rysgaard & Valerie Eveloy, 2020. "Discussion on A high‐resolution bilevel skew‐t stochastic generator for assessing Saudi Arabia's wind energy resources," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
    9. Fahd A. Alturki & Emad Mahrous Awwad, 2021. "Sizing and Cost Minimization of Standalone Hybrid WT/PV/Biomass/Pump-Hydro Storage-Based Energy Systems," Energies, MDPI, vol. 14(2), pages 1-20, January.
    10. Paxis Marques João Roque & Shyama Pada Chowdhury & Zhongjie Huan, 2021. "Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study," Energies, MDPI, vol. 14(14), pages 1-22, July.
    11. Dhiman, Harsh S. & Deb, Dipankar, 2020. "Wake management based life enhancement of battery energy storage system for hybrid wind farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).

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