Theoretical study of analytical wake models for yawed wind turbines
The present study will explore some of the most relevant engineering wake models for yawed wind turbines developed to date. Compared to wind tunnel tests and numerical simulations, analytical wake models have greater applications in real wind farm control and optimization because of their simplicity...
- Autores:
-
Plata Uribe, Camilo Andrés
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/74704
- Acceso en línea:
- https://hdl.handle.net/1992/74704
- Palabra clave:
- Yawed wind turbine
Analytical wake model
Wake deflection
Wake steering
High-fidelity SOWFA simulation
Ingeniería
- Rights
- openAccess
- License
- Attribution 4.0 International
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Theoretical study of analytical wake models for yawed wind turbines |
| title |
Theoretical study of analytical wake models for yawed wind turbines |
| spellingShingle |
Theoretical study of analytical wake models for yawed wind turbines Yawed wind turbine Analytical wake model Wake deflection Wake steering High-fidelity SOWFA simulation Ingeniería |
| title_short |
Theoretical study of analytical wake models for yawed wind turbines |
| title_full |
Theoretical study of analytical wake models for yawed wind turbines |
| title_fullStr |
Theoretical study of analytical wake models for yawed wind turbines |
| title_full_unstemmed |
Theoretical study of analytical wake models for yawed wind turbines |
| title_sort |
Theoretical study of analytical wake models for yawed wind turbines |
| dc.creator.fl_str_mv |
Plata Uribe, Camilo Andrés |
| dc.contributor.advisor.none.fl_str_mv |
González Mancera, Andrés Leónardo |
| dc.contributor.author.none.fl_str_mv |
Plata Uribe, Camilo Andrés |
| dc.contributor.jury.none.fl_str_mv |
González Mancera, Andrés Leónardo |
| dc.subject.keyword.eng.fl_str_mv |
Yawed wind turbine Analytical wake model Wake deflection Wake steering |
| topic |
Yawed wind turbine Analytical wake model Wake deflection Wake steering High-fidelity SOWFA simulation Ingeniería |
| dc.subject.keyword.none.fl_str_mv |
High-fidelity SOWFA simulation |
| dc.subject.themes.spa.fl_str_mv |
Ingeniería |
| description |
The present study will explore some of the most relevant engineering wake models for yawed wind turbines developed to date. Compared to wind tunnel tests and numerical simulations, analytical wake models have greater applications in real wind farm control and optimization because of their simplicity and high efficiency. These engineering models were implemented in PyWake [9], an open source and Python-based wind farm simulation tool developed at DTU capable of computing flow fields, power production of individual turbines, as well as the Annual Energy Production (AEP) of a wind farm. This software can quantify the interaction between turbines and calculate wake propagation inside a wind farm with excellent efficiency. In addition, high-fidelity SOWFA simulations, performed by the CL-Windcon project, were used to validate the models mentioned above and their implementation. |
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2024 |
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2024-07-25T20:39:57Z |
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2024-07-25T20:39:57Z |
| dc.date.issued.none.fl_str_mv |
2024-07-25 |
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Trabajo de grado - Pregrado |
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info:eu-repo/semantics/bachelorThesis |
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[1] A. Jimenez, A. Crespo, and E. Migoya, “Application of a les technique to characterize the wake deflection of a wind turbine in yaw,” Wind Energy, vol. 13, no. 6, pp. 559–572, 2010. [2] M. Bastankhah and F. Porté-Agel, “Experimental and theoretical study of wind turbine wakes in yawed conditions,” Journal of Fluid Mechanics, vol. 806, pp. 506–541, 11 2016. [3] C. R. Shapiro, D. F. Gayme, and C. Meneveau, “Modelling yawed wind turbine wakes: a lifting line approach,” Journal of Fluid Mechanics, vol. 841, p. R1, 4 2018. [4] G.W. Qian and T. Ishihara, “A new analytical wake model for yawed wind turbines,” Energies, vol. 11, 2018. [5] F. Blondel, M. Cathelain, P.-A. Joulin, and P. Bozonnet, “An adaptation of the super-gaussian wake model for yawed wind turbines,” Journal of Physics: Conference Series, vol. 1618, no. 6, 2020. [6] J. King, P. Fleming, R. King, L. A. Martínez-Tossas, C. J. Bay, R. Mudafort, and E. Simley, “Controloriented model for secondary effects of wake steering,” Wind Energy Science, vol. 6, 2021. [7] C. J. Bay, P. Fleming, B. Doekemeijer, J. King, M. Churchfield, and R. Mudafort, “Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model,”Wind Energy Science, vol. 8, no. 3, pp. 401–419, 2023. [8] J. K. Kaldellis, P. Triantafyllou, and P. Stinis, “Critical evaluation of wind turbines’ analytical wake models,” Renewable and Sustainable Energy Reviews, vol. 144, p. 110991, 2021. [9] P. v. d. L. Mads M. Pedersen, Alexander M. Forsting, “Pywake 2.5.0: An open-source wind farm simulation tool,” 2 2023. [10] G. W. E. C. (GWEC), “Global wind report 2024,” tech. rep., Global Wind Energy Council, 2024. [11] Z. Deng, C. Xu, X. Han, Z. Cheng, and F. Xue, “Decentralized yaw optimization for maximizing wind farm production based on deep reinforcement learning,” Energy Conversion and Management, vol. 286, p. 117031, 6 2023. [12] M. F. Howland, J. B. Quesada, J. J. P. Martínez, F. P. Larrañaga, N. Yadav, J. S. Chawla, V. Sivaram, and J. O. Dabiri, “Collective wind farm operation based on a predictive model increases utility-scale energy production,” Nature Energy, vol. 7, 2022. [13] N. Jenkins, T. Burton, E. Bossanyi, D. Sharpe, and M. Graham, Wake effects and wind farm control, ch. 9, pp. 637–663. John Wiley & Sons, Ltd, 2021. [14] N. O. Jensen, A note on wind generator interaction, vol. 2411. Citeseer, 1983. [15] N. G. Nygaard, S. T. Steen, L. Poulsen, and J. G. Pedersen, “Modelling cluster wakes and wind farm blockage,” Journal of Physics: Conference Series, vol. 1618, p. 062072, sep 2020. [16] M. Bastankhah and F. Porté-Agel, “A new analytical model for wind-turbine wakes,” Renewable Energy, vol. 70, pp. 116–123, 10 2014. [17] A. Niayifar and F. Porté-Agel, “Analytical modeling of wind farms: A new approach for power prediction,” Energies, vol. 9, no. 9, 2016. [18] H. Zong and F. Porté-Agel, “A momentum-conserving wake superposition method for wind farm power prediction,” Journal of Fluid Mechanics, vol. 889, p. A8, 2020. [19] F. Blondel and M. Cathelain, “An alternative form of the super-gaussian wind turbine wake model,” Wind Energy Science, vol. 5, 2020. [20] F. Blondel, “Brief communication: A momentum-conserving superposition method applied to the super-gaussian wind turbine wake model,” Wind Energy Science, vol. 8, no. 2, 2023. [21] A. Keane, “Advancement of an analytical double-gaussian full wind turbine wake model,” Renewable Energy, vol. 171, pp. 687–708, 6 2021. [22] Q. M. Soesanto, T. Yoshinaga, and A. Iida, “Anisotropic double-gaussian analytical wake model for an isolated horizontal-axis wind turbine,” Energy Science and Engineering, vol. 10, 2022. [23] P. M. O. Gebraad, F. W. Teeuwisse, J. W. van Wingerden, P. A. Fleming, S. D. Ruben, J. R. Marden, and L. Y. Pao, “Wind plant power optimization through yaw control using a parametric model for wake effects - a cfd simulation study,” Wind Energy, vol. 19, no. 1, pp. 95–114, 2014. [24] P. A. Fleming, A. Ning, P. M. Gebraad, and K. Dykes, “Wind plant system engineering through optimization of layout and yaw control,” Wind Energy, vol. 19, 2016. [25] M. F. Howland, J. Bossuyt, L. A. Martínez-Tossas, J. Meyers, and C. Meneveau, “Wake structure in actuator disk models of wind turbines in yaw under uniform inflow conditions,” Journal of Renewable and Sustainable Energy, vol. 8, no. 4, 2016. [26] T. Ishihara and G. W. Qian, “A new gaussian-based analytical wake model for wind turbines considering ambient turbulence intensities and thrust coefficient effects,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 177, 2018. [27] M. Bastankhah, C. R. Shapiro, S. Shamsoddin, D. F. Gayme, and C. Meneveau, “A vortex sheet based analytical model of the curled wake behind yawed wind turbines,” Journal of Fluid Mechanics, vol. 933, 2022. [28] P. Fleming, J. Annoni, M. Churchfield, L. A. Martínez-Tossas, K. Gruchalla, M. Lawson, and P. Moriarty, “A simulation study demonstrating the importance of large-scale trailing vortices in wake steering,” Wind Energy Science, no. 1, pp. 243–255, 2018. [29] M. Abkar and F. Porté-Agel, “Influence of atmospheric stability on wind-turbine wakes: A large-eddy simulation study,” Physics of Fluids, vol. 27, no. 3, p. 035104, 2015. [30] L. A. Martínez-Tossas, J. Annoni, P. A. Fleming, and M. J. Churchfield, “The aerodynamics of the curled wake: a simplified model in view of flow control,” Wind Energy Science, vol. 4, no. 1, pp. 127–138, 2019. [31] M. Bastankhah, B. L. Welch, L. A. Martínez-Tossas, J. King, and P. Fleming, “Analytical solution for the cumulative wake of wind turbines in wind farms,” Journal of Fluid Mechanics, vol. 911, 2021. [32] P. M. Gebraad, F. W. Teeuwisse, J. W. V. Wingerden, P. A. Fleming, S. D. Ruben, J. R. Marden, and L. Y. Pao, “Wind plant power optimization through yaw control using a parametric model for wake effects - a cfd simulation study,” Wind Energy, vol. 19, 2016. [33] R. P. Coleman, A. M. Feingold, and C.W. Stempin, “Evaluation of the induced-velocity field of an idealized helicopter rotor,” in NACA Advanced Restricted Reports, NATIONAL AERONAUTICS AND SPACE ADMINISTRATION HAMPTON VA LANGLEY RESEARCH CENTER, 1945. [34] J. H. W. Lee and V. H. Chu, Turbulent Jets and Plumes: A Lagrangian Approach. Kluwer Academic Publishers, 2003. [35] P. A. Fleming, P. M. Gebraad, S. Lee, J.-W. van Wingerden, K. Johnson, M. Churchfield, J. Michalakes, P. Spalart, and P. Moriarty, “Evaluating techniques for redirecting turbine wakes using sowfa,” Renewable Energy, vol. 70, pp. 211–218, 2014. Special issue on aerodynamics of offshore wind energy systems and wakes. |
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Al consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autoresAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2González Mancera, Andrés Leónardovirtual::19227-1Plata Uribe, Camilo AndrésGonzález Mancera, Andrés Leónardovirtual::19228-12024-07-25T20:39:57Z2024-07-25T20:39:57Z2024-07-25https://hdl.handle.net/1992/74704instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/The present study will explore some of the most relevant engineering wake models for yawed wind turbines developed to date. Compared to wind tunnel tests and numerical simulations, analytical wake models have greater applications in real wind farm control and optimization because of their simplicity and high efficiency. These engineering models were implemented in PyWake [9], an open source and Python-based wind farm simulation tool developed at DTU capable of computing flow fields, power production of individual turbines, as well as the Annual Energy Production (AEP) of a wind farm. This software can quantify the interaction between turbines and calculate wake propagation inside a wind farm with excellent efficiency. In addition, high-fidelity SOWFA simulations, performed by the CL-Windcon project, were used to validate the models mentioned above and their implementation.A theoretical study on analytical wake models for yawed wind turbines is presented. The primary objective is to address the complex flow dynamics in wind farms, focusing on the effects of yawed wind turbines on wake behavior. The research tackles the problem by evaluating existing analytical models that predict the wake effects of yawed wind turbines. The study systematically compares various models, such as those by Jimenez [1], Bastankah & Porte Agel [2], Shapiro [3], Qian & Ishihara [4], and Blondel [5] among others, using a combination of theoretical frameworks and computational simulations. Key findings demonstrate that models incorporating secondary steering and multi-turbine effects, such as the Gauss-Curl Hybrid model [6] and the Cumulative Curl model [7], provide a more accurate representation of wake interactions in large wind farms. This study aims to integrate state-of-the-art analytical models in PyWake, a Python-based wind farm simulation tool, due to its versatility on coupling between engineering models. Finally this models are validated against high-fidelity SOWFA simulations, highlighting their improved predictive capabilities in capturing wake deflection and energy production impacts. The significance of these findings lies in their potential to optimize wind farm performance through better wake management, ultimately contributing to more efficient and sustainable wind energy production.PregradoConversión de Energía70 páginasapplication/pdfengUniversidad de los AndesIngeniería MecánicaFacultad de IngenieríaDepartamento de Ingeniería MecánicaTheoretical study of analytical wake models for yawed wind turbinesTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPYawed wind turbineAnalytical wake modelWake deflectionWake steeringHigh-fidelity SOWFA simulationIngeniería[1] A. Jimenez, A. Crespo, and E. Migoya, “Application of a les technique to characterize the wake deflection of a wind turbine in yaw,” Wind Energy, vol. 13, no. 6, pp. 559–572, 2010.[2] M. Bastankhah and F. Porté-Agel, “Experimental and theoretical study of wind turbine wakes in yawed conditions,” Journal of Fluid Mechanics, vol. 806, pp. 506–541, 11 2016.[3] C. R. Shapiro, D. F. Gayme, and C. Meneveau, “Modelling yawed wind turbine wakes: a lifting line approach,” Journal of Fluid Mechanics, vol. 841, p. R1, 4 2018.[4] G.W. Qian and T. Ishihara, “A new analytical wake model for yawed wind turbines,” Energies, vol. 11, 2018.[5] F. Blondel, M. Cathelain, P.-A. Joulin, and P. Bozonnet, “An adaptation of the super-gaussian wake model for yawed wind turbines,” Journal of Physics: Conference Series, vol. 1618, no. 6, 2020.[6] J. King, P. Fleming, R. King, L. A. Martínez-Tossas, C. J. Bay, R. Mudafort, and E. Simley, “Controloriented model for secondary effects of wake steering,” Wind Energy Science, vol. 6, 2021.[7] C. J. Bay, P. Fleming, B. Doekemeijer, J. King, M. Churchfield, and R. Mudafort, “Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model,”Wind Energy Science, vol. 8, no. 3, pp. 401–419, 2023.[8] J. K. Kaldellis, P. Triantafyllou, and P. Stinis, “Critical evaluation of wind turbines’ analytical wake models,” Renewable and Sustainable Energy Reviews, vol. 144, p. 110991, 2021.[9] P. v. d. L. Mads M. Pedersen, Alexander M. Forsting, “Pywake 2.5.0: An open-source wind farm simulation tool,” 2 2023.[10] G. W. E. C. (GWEC), “Global wind report 2024,” tech. rep., Global Wind Energy Council, 2024.[11] Z. Deng, C. Xu, X. Han, Z. Cheng, and F. Xue, “Decentralized yaw optimization for maximizing wind farm production based on deep reinforcement learning,” Energy Conversion and Management, vol. 286, p. 117031, 6 2023.[12] M. F. Howland, J. B. Quesada, J. J. P. Martínez, F. P. Larrañaga, N. Yadav, J. S. Chawla, V. Sivaram, and J. O. Dabiri, “Collective wind farm operation based on a predictive model increases utility-scale energy production,” Nature Energy, vol. 7, 2022.[13] N. Jenkins, T. Burton, E. Bossanyi, D. Sharpe, and M. Graham, Wake effects and wind farm control, ch. 9, pp. 637–663. John Wiley & Sons, Ltd, 2021.[14] N. O. Jensen, A note on wind generator interaction, vol. 2411. Citeseer, 1983.[15] N. G. Nygaard, S. T. Steen, L. Poulsen, and J. G. Pedersen, “Modelling cluster wakes and wind farm blockage,” Journal of Physics: Conference Series, vol. 1618, p. 062072, sep 2020.[16] M. Bastankhah and F. Porté-Agel, “A new analytical model for wind-turbine wakes,” Renewable Energy, vol. 70, pp. 116–123, 10 2014.[17] A. Niayifar and F. Porté-Agel, “Analytical modeling of wind farms: A new approach for power prediction,” Energies, vol. 9, no. 9, 2016.[18] H. Zong and F. Porté-Agel, “A momentum-conserving wake superposition method for wind farm power prediction,” Journal of Fluid Mechanics, vol. 889, p. A8, 2020.[19] F. Blondel and M. Cathelain, “An alternative form of the super-gaussian wind turbine wake model,” Wind Energy Science, vol. 5, 2020.[20] F. Blondel, “Brief communication: A momentum-conserving superposition method applied to the super-gaussian wind turbine wake model,” Wind Energy Science, vol. 8, no. 2, 2023.[21] A. Keane, “Advancement of an analytical double-gaussian full wind turbine wake model,” Renewable Energy, vol. 171, pp. 687–708, 6 2021.[22] Q. M. Soesanto, T. Yoshinaga, and A. Iida, “Anisotropic double-gaussian analytical wake model for an isolated horizontal-axis wind turbine,” Energy Science and Engineering, vol. 10, 2022.[23] P. M. O. Gebraad, F. W. Teeuwisse, J. W. van Wingerden, P. A. Fleming, S. D. Ruben, J. R. Marden, and L. Y. Pao, “Wind plant power optimization through yaw control using a parametric model for wake effects - a cfd simulation study,” Wind Energy, vol. 19, no. 1, pp. 95–114, 2014.[24] P. A. Fleming, A. Ning, P. M. Gebraad, and K. Dykes, “Wind plant system engineering through optimization of layout and yaw control,” Wind Energy, vol. 19, 2016.[25] M. F. Howland, J. Bossuyt, L. A. Martínez-Tossas, J. Meyers, and C. Meneveau, “Wake structure in actuator disk models of wind turbines in yaw under uniform inflow conditions,” Journal of Renewable and Sustainable Energy, vol. 8, no. 4, 2016.[26] T. Ishihara and G. W. Qian, “A new gaussian-based analytical wake model for wind turbines considering ambient turbulence intensities and thrust coefficient effects,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 177, 2018.[27] M. Bastankhah, C. R. Shapiro, S. Shamsoddin, D. F. Gayme, and C. Meneveau, “A vortex sheet based analytical model of the curled wake behind yawed wind turbines,” Journal of Fluid Mechanics, vol. 933, 2022.[28] P. Fleming, J. Annoni, M. Churchfield, L. A. Martínez-Tossas, K. Gruchalla, M. Lawson, and P. Moriarty, “A simulation study demonstrating the importance of large-scale trailing vortices in wake steering,” Wind Energy Science, no. 1, pp. 243–255, 2018.[29] M. Abkar and F. Porté-Agel, “Influence of atmospheric stability on wind-turbine wakes: A large-eddy simulation study,” Physics of Fluids, vol. 27, no. 3, p. 035104, 2015.[30] L. A. Martínez-Tossas, J. Annoni, P. A. Fleming, and M. J. Churchfield, “The aerodynamics of the curled wake: a simplified model in view of flow control,” Wind Energy Science, vol. 4, no. 1, pp. 127–138, 2019.[31] M. Bastankhah, B. L. Welch, L. A. Martínez-Tossas, J. King, and P. Fleming, “Analytical solution for the cumulative wake of wind turbines in wind farms,” Journal of Fluid Mechanics, vol. 911, 2021.[32] P. M. Gebraad, F. W. Teeuwisse, J. W. V. Wingerden, P. A. Fleming, S. D. Ruben, J. R. Marden, and L. Y. Pao, “Wind plant power optimization through yaw control using a parametric model for wake effects - a cfd simulation study,” Wind Energy, vol. 19, 2016.[33] R. P. Coleman, A. M. Feingold, and C.W. Stempin, “Evaluation of the induced-velocity field of an idealized helicopter rotor,” in NACA Advanced Restricted Reports, NATIONAL AERONAUTICS AND SPACE ADMINISTRATION HAMPTON VA LANGLEY RESEARCH CENTER, 1945. [34] J. H. W. Lee and V. H. Chu, Turbulent Jets and Plumes: A Lagrangian Approach. Kluwer Academic Publishers, 2003.[35] P. A. Fleming, P. M. Gebraad, S. Lee, J.-W. van Wingerden, K. Johnson, M. Churchfield, J. Michalakes, P. Spalart, and P. Moriarty, “Evaluating techniques for redirecting turbine wakes using sowfa,” Renewable Energy, vol. 70, pp. 211–218, 2014. 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