Wind and Structures

  • 제39권3호
  • /
  • Pages.163-174
  • /
  • 2024
  • /
  • 1226-6116(pISSN)
  • /
  • 1598-6225(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Wind states and power curve modeling: A case study for La Rumorosa I Wind Farm

  • Jesus O. Inzunza Castro (Universidad Autonoma de Baja California, Facultad de Ingenieria) ;
  • Alexis Acuna Ramirez (Universidad Autonoma de Baja California, Facultad de Ingenieria) ;
  • Marlene Zamora Machado (Universidad Autonoma de Baja California, Facultad de Ingenieria) ;
  • Magali Arellano Vazquez (INFOTEC, Centro de Investigacion e Innovacion en TIC) ;
  • Noemi Lizarraga Osuna (Universidad Autonoma de Baja California, Facultad de Ingenieria)
  • 투고 : 2023.02.24
  • 심사 : 2024.08.17
  • 발행 : 2024.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper analyzes La Rumorosa I Wind Farm's wind states and their characteristics in the operation of two wind turbines over the course of one year of records. This information identifies the impact of wind states on wind power output. The study used the Gaussian Mixture Model to classify the occurrence and frequency of the dominant wind states in the generation of energy from the turbines. Results were obtained for mesoscale wind states and local scale wind states, such as cold fronts and Santa Ana winds, as well as daytime, nighttime and hot days, respectively, which were statistically analyzed to determine their relationship to power output by generating power and power coefficient curves. Between the cut-in speed and the rated speed of the wind turbines, cold fronts show higher efficiency, unlike nighttime wind states, which are the most efficient past the rated speed. In addition, cold fronts are also those that occur to the greatest extent, contributing 31.26% of the energy produced per year, compared with the Santa Ana winds, which occur to a lesser extent; however, they contribute 22.11% of the energy produced per year.

키워드

과제정보

To Baja California State Energy Commission for providing data from the La Rumorosa I Wind Farm, and the first author thanks the National Council for the Humanities, Sciences and Technologies (CONAHCyT for its acronym in Spanish) for the doctoral scholarship.

참고문헌

  1. Abatzoglou, J.T., Barbero, R. and Nauslar, N.J. (2013), "Diagnosing Santa Ana winds in Southern California with synoptic-scale analysis", Weather Forecasting, 28, 704-710. https://doi.org/10.1175/WAF-D-13-00002.1.
  2. Ahrens, C.D. and Henson, R. (2021), Meteorology Today: An Introduction To Weather, Climate, And The Environment, Cengage Learning, Boston, MA, USA.
  3. Arellano V.M. (2020), WindC1IA. Windows/Linux. Aguascalientes. Available online: https://github.com/estudiovientos/saw/releases/tag/windc1ai (accessed on 12 12 2023).
  4. Arellano V.M., Zamora M.M., Kuri R.E.G., Robles P.M. and Jaramillo S.O.A. (2017), "Dynamical analysis of wind data for detection and classification.", Conference on Complex Systems, Quintana Roo, MX, September.
  5. Arellano V.M., Zamora M.M., Robles P.M. and Jaramillo S.O.A. (2020), "Favorable wind states in wind energy production at La Rumorosa I wind farm.", J. Phys. Conference Series, 1618, 062071. https://doi.org/10.1088/1742-6596/1618/6/062071.
  6. Carrillo, C., Obando M.A.F., Cidras, J. and Diaz C.E. (2013), "Review of power curve modelling for wind turbines.", Renew. Sustain. Energy Rev., 21, 572-581. https://doi.org/10.1016/j.rser.2013.01.012.
  7. Diario Oficial de la Federacion. (2013), DECRETO por el que se reforman y adicionan diversas disposiciones de la Constitucion Politica de los Estados Unidos Mexicanos, en Materia de Energia, 17.
  8. Diario Oficial de la Federacion. (2017), ACUERDO por el que se emite el Manual de Pronosticos, 18.
  9. Diario Oficial de la Federacion. (2018), ACUERDO por el que se emite el Manual para el Desarrollo de las Reglas del Mercado, 5.
  10. Emeis, S. (2018), Wind Energy Meteorology. Springer Cham.
  11. Fernandez D.P. (1993), Energia eolica, Universidad de Cantabria, Santander, ES.
  12. Freitas de Andrade, C., Rocha V.F., Silveira M.M.V., Ferreira dos Santos, L., Costa R.P.A. and Santos G.K. (2018), "Application of the cuckoo search in the adjustment of weibull curves for wind energy using wind data of Petrolina city.", IEEE Latin Amer. Transact., 16, 2513-2520. https://doi.org/10.1109/TLA.2018.8795130.
  13. Gamesa G87/2000 (2000), Manufacturers and wind turbines. Available online: https://www.thewindpower.net/turbine_es_46_gamesa_g87-2000.php (accessed on 23 08 2020).
  14. Global Modeling and Assimilation Office (2020), Available online: https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/ (accessed on 22 10 2020).
  15. Hellman, G. (1916), "uber die Bewerbung der Luft in den untersten schichten der atmosphere.", Meteorologische Zeitschrift, 34, 273-285.
  16. Hong, P. and Quin, Z. (2022). "Distributed active power optimal dispatching of wind farm cluster considering wind power uncertainty", Energies, 15, 2706. https://doi.org/10.3390/en15072706.
  17. Hughes, M. and Hall, A. (2010), "Local and synoptic mechanisms causing Southern California's Santa Ana winds", Climate Dyn., 34, 847-857. https://doi.org/10.1007/s00382-009-0650-4.
  18. Hutchinson, M. and Zhao, F. (2023), "Global Wind Report 2023", Global Wind Energy Council, Brussels, Belgium, 97-109.
  19. IEC (2017), Wind energy generation systems - Part 12-1: Power performance measurements of electricity producing wind turbines, IEC 61400-12-1:2017.
  20. Jaramillo S.O.A. and Borja, M.A. (2004), "Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case", Renew. Energy, 29, 1613-1630. https://doi.org/10.1016/j.renene.2004.02.001.
  21. Jourdier, B. and Drobinski, P. (2017), "Errors in wind resource and energy yield assessments based on the weibull distribution", Annales Geophysic., 35, 691-700. https://doi.org/10.5194/angeo-35-691-2017.
  22. Justus, C.G., Hargraves, W.R., Mikhail, A. and Graber, D. (1978), "Methods for estimating wind speed frequency distributions", J. Appl. Meteorol. Climatology, 17, 350-353. https://doi.org/10.1175/1520-0450(1978)017<0350:MFEWSF>2.0.CO;2.
  23. Lee, J. and Zhao, F. (2024), "Global Wind Report 2024", Global Wind Energy Council, Brussels, Belgium, 26-149.
  24. Lee, J.C., Stuart, P., Clifton, A., Fields, M.J., Perr-Sauer, J., Williams, L. and Housley, P. (2020), "The power curve working group's assessment of wind turbine power performance prediction methods", Wind Energy Sci., 5(1), 199-223. https://doi.org/ 10.5194/wes-5-199-2020.
  25. Lutgens, F.K., Tarbuck, E.J., Herman, R.L. and Tasa, D. (2018). The Atmosphere: An Introduction To Meteorology, Pearson, Glenview, IL, USA.
  26. Manwell, J.F., McGowan, J.G. and Rogers, A.L. (2009), Wind Energy Explained: Theory, Design And Application, Wiley, Chichester, U.K.
  27. Milligan, M., Schwartz, M.N. and Wan Y. (2003), Statistical Wind Power Forecasting Models: Results for U.S. Wind Farm.
  28. Optis, M. and Perr-Sauer, J. (2019). "The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production.", Renew. Sustain. Energy Rev., 112, 27-41. https://doi.org/10.1016/j.rser.2019.05.031.
  29. Rolinski, T., Capps, S.B. and Zhuang, W. (2019), "Santa Ana Winds: A descriptive climatology", Weather Forecasting, 34, 257-275. https://doi.org/10.1175/WAF-D-18-0160.1.
  30. Romero, R.C., Zavala H.J. and Raga, G.B. (2007), "Midsummer gap winds and low-level circulation over the eastern tropical Pacific", J. Climate, 20, 3768-3784. https://doi.org/10.1175/JCLI4220.1.
  31. Sanchez P.P.A., Robles P.M. and Jaramillo S.O.A. (2016), "Real time Markov chains: Wind states in anemometric data", J. Renew. Sustain. Energy, 8, 023304. https://doi.org/10.1063/1.4943120.
  32. Shen, R., Li, B., Li, K., Yan, B. and Zhang, Y. (2024), "Reconstruction of wind speed fields in mountainous areas using a full convolutional neural network", Wind Struct., 38, 231-244. https://doi.org/10.12989/WAS.2024.38.4.231.
  33. Tande, J.O. and Fransden, S. (1995), "Estimation of cost and value of energy from wind turbines", Proceedings of the Wind Energy Conversion 1994, Stirling, United Kingdom, June.
  34. Westerling, A.L., Cayan, D.R., Brown, T.J., Hall, B.L. and Riddle, L.G. (2004), "Climate, Santa Ana winds and autumn wildfires in southern California", EOS Transact. Amer. Geophys. Union, 85, 289. https://doi.org/10.1029/2004EO310001.
  35. Xiao, X. and Waddell, C., Hamilton, C. and Xiao, H. (2022), "Quality prediction and control in wire arc additive manufacturing via novel machine learning framework", Micromachines, 13, 137. https://doi.org/10.3390/mi13010137.
  36. Xiao, X., Roh, B.-M. and Hamilton, C. (2022), "Porosity management and control in powder bed fusion process through process-quality interactions", CIRP J. Manufact. Sci. Technol., 38. 120-128. https://doi.org/10.1016/j.cirpj.2022.04.005.
  37. Zafirakis, D.P., Paliatsos, A.G. and Kaldellis, J.K. (2012), "Energy yield of contemporary wind turbines", Comprehensive Renew. Energy, 2, 113-168. https://doi.org/10.1016/B978-0-12-819727-1.00152-7.
  38. Zamora M.M. (2016), "Variabilidad del potencial eolico y su relacion con fenomenos meteorologicos en Baja California", Ph.D. Dissertation, Universidad Autonoma de Baja California, Mexicali, Baja California, MX.
  39. Zamora, M., Lambert, A., Garcia, O., Jaramillo, O., Leyva, E., Reynaga, R., Herrera, R., Lizarraga, N. and Anguiano, R. (2023), "Classification of Santa Ana winds for the evaluation of their wind potential in La Rumorosa, Baja California, Mexico. Ciencias marinas, 49(1), 3358.
  40. Zehtabiyan R.N., Iosifidis, A. and Abkar, M. (2022), "Data-driven fluid mechanics of wind farms: A review", J. Renew. Sustain. Energy, 14, 032703. https://doi.org/10.1063/5.0091980.