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Research Articles

Analysis and Comparison of Weibull Parameters for Wind Energy Potential Using Different Estimation Methods: A Case Study of Isparta Province in Turkey

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Pages 1829-1845 | Received 21 Feb 2023, Accepted 01 May 2023, Published online: 15 May 2023

References

  • O. Ellabban, H. Abu-Rub, and F. Blaabjerg, “Renewable energy sources: Current status, future prospects and their enabling technology,” Renew. Sustain. Energy Rev., vol. 39, pp. 748–764, Nov. 2014. DOI: 10.1016/j.rser.2014.07.113.
  • A. Mostafaeipour, M. Jadidi, K. Mohammadi, and A. Sedaghat, “An analysis of wind energy potential and economic evaluation, in Zahedan, Iran,” Renew. Sustain. Energy Rev., vol. 30, pp. 641–650, Feb. 2014. DOI: 10.1016/j.rser.2013.11.016.
  • R. Pallabazzer, “Parametric analysis of wind siting efficiency,” J. Wind Eng. Ind. Aerodyn., vol. 91, no. 11, pp. 1329–1352, Nov. 2003. DOI: 10.1016/j.jweia.2003.08.002.
  • S. Rehman et al., “Wind and wind power characteristics of the eastern and southern coastal and northern inland regions, South Africa,” Environ. Sci. Pollut. Res. Int., vol. 29, no. 57, pp. 85842–85854, Dec. 2022. DOI: 10.1007/s11356-021-14276-9.
  • S. F. Khahro, K. Tabbassum, A. M. Soomro, L. Dong, and X. Liao, “Evaluation of wind power production prospective and Weibull parameter estimation methods for Babaurband, Sindh Pakistan,” Energy Convers. Manage., vol. 78, pp. 956–967, Feb. 2014. DOI: 10.1016/j.enconman.2013.06.062.
  • K. Mohammadi, O. Alavi, A. Mostafaeipour, N. Goudarzi, and M. Jalilvand, “Assessing different parameters estimation methods of Weibull distribution to compute wind power density,” Energy Convers. Manage., vol. 108, pp. 322–335, Jan. 2016. DOI: 10.1016/j.enconman.2015.11.015.
  • I. Usta, I. Arık, I. Yenilmez, and Y. M. Kantar, “A new estimation approach based on moments for estimating Weibull parameters in wind power applications,” Energy Convers. Manage., vol. 164, pp. 570–578, May 2018. DOI: 10.1016/j.enconman.2018.03.033.
  • D. Kang, K. Ko, and J. Huh, “Comparative study of different methods for estimating Weibull parameters: A case study on Jeju Island, South Korea,” Energies, vol. 11, no. 2, pp. 356, Feb. 2018. DOI: 10.3390/en11020356.
  • J. A. Guarienti et al., “Performance analysis of numerical methods for determining Weibull distribution parameters applied to wind speed in Mato Grosso do Sul, Brazil,” Sustain. Energy Technol. Assess., vol. 42, pp. 100854, Dec. 2020. DOI: 10.1016/j.seta.2020.100854.
  • S. Kang, A. Khanjari, S. You, and J. Lee, “Comparison of different statistical methods used to estimate Weibull parameters for wind speed contribution in nearby an offshore site, Republic of Korea,” Energy Rep., vol. 7, pp. 7358–7373, Nov. 2021. DOI: 10.1016/j.egyr.2021.10.078.
  • Y. A. Kaplan, “Comparison of the performance of the methods used to find Weibull parameters at different heights,” Arab. J. Sci. Eng., vol. 46, no. 12, pp. 12145–12153, Dec. 2021. DOI: 10.1007/s13369-021-05866-3.
  • A. E. Onay, E. Dokur, and M. Kurban, “Performance comparison of new generation parameter estimation methods for Weibull distribution to compute wind energy density,” Elektron. Elektrotech., vol. 27, no. 5, pp. 41–48, Oct. 2021. DOI: 10.5755/j02.eie.28919.
  • B. Yaniktepe, O. Kara, I. Aladağ, and C. Ozturk, “Comparison of eight methods of Weibull distribution for determining the best-fit distribution parameters with wind data measured from the met-mast,” Environ. Sci. Pollut. Res. Int., vol. 30, no. 4, pp. 9576–9590, Sept. 2023. DOI: 10.1007/s11356-022-22777-4.
  • B. Pandeya, B. Prajapati, A. Khanal, B. Regmi, and S. R. Shakya, “Estimation of wind energy potential and comparison of six Weibull parameters estimation methods for two potential locations in Nepal,” Int. J. Energy Environ. Eng., vol. 13, no. 3, pp. 955–966, Sept. 2022. DOI: 10.1007/s40095-021-00444-7.
  • T. Arslan, Y. M. Bulut, and A. Yavuz, “Comparative study of numerical methods for determining Weibull parameters for wind energy potential,” Renew. Sustain. Energy Rev., vol. 40, pp. 820–825, Dec. 2014. DOI: 10.1016/j.rser.2014.08.009.
  • M. Celeska et al., “Estimation of Weibull parameters from wind measurement data by comparison of statistical methods,” presented at the IEEE EUROCON 2015 – Int. Conf. on Comput. as a Tool (EUROCON), Salamanca, Spain, Sept. 2015. pp. 1–6. DOI: 10.1109/EUROCON.2015.7313684.
  • M. A. Baseer, J. P. Meyer, S. Rehman, and M. M. Alam, “Wind power characteristics of seven data collection sites in Jubail, Saudi Arabia using Weibull parameters,” Renew. Energy, vol. 102, pp. 35–49, Mar. 2017. DOI: 10.1016/j.renene.2016.10.040.
  • P. K. Chaurasiya, S. Ahmed, and V. Warudkar, “Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument,” Alex. Eng. J., vol. 57, no. 4, pp. 2299–2311, Dec. 2018. DOI: 10.1016/j.aej.2017.08.008.
  • H. Patidar, V. Shende, P. Baredar, and A. Soni, “Comparative study of offshore wind energy potential assessment using different Weibull parameters estimation methods,” Environ. Sci. Pollut. Res. Int., vol. 29, no. 30, pp. 46341–46356, Jun. 2022. DOI: 10.1007/s11356-022-19109-x.
  • P. T. Kapen, M. J. Guoajio, and D. Yemele, “Analysis and efficient comparison of ten numerical methods in estimating Weibull parameters for wind energy potential: Application to the city of Bafoussam, Cameroon,” Renew. Energy, vol. 159, pp. 1188–1198, Oct. 2020. DOI: 10.1016/j.renene.2020.05.185.
  • U. C. Ben, A. E. Akpan, C. C. Mbonu, and C. H. Ufuafounye, “Integrated technical analysis of wind speed data for wind energy potential assessment in parts of southern and central Nigeria,” Clean. Eng. Technol., vol. 2, pp. 100049, Jun. 2021. DOI: 10.1016/j.clet.2021.100049.
  • M. M. Salah, A. G. Abo-Khalil, and R. P. Praveen, “Wind speed characteristics and energy potential for selected sites in Saudi Arabia,” J. King Saud Univ., vol. 33, no. 2, pp. 119–128, Feb. 2021. DOI: 10.1016/j.jksues.2019.12.006.
  • L. Bilir, M. Imir, Y. Devrim, and A. Albostan, “Seasonal and yearly wind speed distribution and wind power density analysis based on Weibull distribution function,” Int. J. Hydrogen Energy, vol. 40, no. 44, pp. 15301–15310, Nov. 2015. DOI: 10.1016/j.ijhydene.2015.04.140.
  • H. R. Alsamamra, S. Salah, J. A. H. Shoqeir, and A. J. Manasra, “A comparative study of five numerical methods for the estimation of Weibull parameters for wind energy evaluation at Eastern Jerusalem, Palestine,” Energy Rep., vol. 8, pp. 4801–4810, Nov. 2022. DOI: 10.1016/j.egyr.2022.03.180.
  • H. Teimourian, M. Abubakar, M. Yildiz, and A. Teimourian, “A comparative study on wind energy assessment distribution models: A case study on Weibull distribution,” Energies, vol. 15, no. 15, pp. 5684, Aug. 2022. DOI: 10.3390/en15155684.
  • A. K. Azad, M. G. Rasul, R. Islam, and I. R. Shishir, “Analysis of wind energy prospect for power generation by three Weibull distribution methods,” Energy Proc., vol. 75, pp. 722–727, Aug. 2015. DOI: 10.1016/j.egypro.2015.07.499.
  • M. M. Riaz and B. H. Khan, “Estimation of Weibull parameters and selection of optimal wind turbine for the development of large offshore wind farm,” presented at the 2019 Int. Conf. on Electr., Electron. and Comput. Eng. (UPCON), Aligarh, India, Nov. 2019, pp. 1–6. DOI: 10.1109/UPCON47278.2019.8980167.
  • K. S. R. Murthy and O. P. Rahi, “Estimation of Weibull parameters using graphical method for wind energy applications,” presented at the 2014 Eighteenth Natl. Power Syst. Conf. (NPSC), Guwahati, India, Dec. 2014, pp. 1–6. DOI: 10.1109/NPSC.2014.7103858.
  • Y. A. Kaplan, “Rayleigh ve Weibull dağılımları kullanılarak Osmaniye bölgesinde rüzgar enerjisinin değerlendirilmesi,” Suleyman Demirel Univ. J. Natural Appl. Sci., vol. 20, pp. 62–71, 2016. DOI: 10.19113/sdufbed.63806.
  • E. Dokur, M. Kurban, and S. Ceyhan, “A novel Information Geometry method for estimating parameters of Weibull speed distribution,” Proc. Estonian Acad. Sci., vol. 67, no. 1, pp. 39–49, Jan. 2018. DOI: 10.3176/proc.2018.1.01.
  • T. C. Carneiro, S. P. Melo, P. C. M. Carvalho, and A. P. S. Braga, “Particle swarm optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region,” Renew. Energy, vol. 86, pp. 751–759, Feb. 2016. DOI: 10.1016/j.renene.2015.08.060.
  • M. Gul, N. Tai, W. Huang, M. H. Nadeem, and M. Yu, “Evaluation of wind energy potential using an optimum approach based on maximum distance metric,” Sustainability, vol. 12, no. 5, pp. 1999, Mar. 2020. DOI: 10.3390/su12051999.
  • M. Wadi and W. Elmasry, “Statistical analysis of wind energy potential using different estimation methods for Weibull parameters: A case study,” Electr. Eng., vol. 103, no. 6, pp. 2573–2594, Dec. 2021. DOI: 10.1007/s00202-021-01254-0.
  • J. Wan et al., “Assessment of wind energy resources in the urat area using optimized Weibull distribution,” Sustain. Energy Technol. Assess., vol. 47, pp. 101351, Oct. 2021. DOI: 10.1016/j.seta.2021.101351.
  • W. Zhao, Z. Zhang, and L. Wang, “Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications,” Eng. Appl. Artif. Intell., vol. 87, pp. 103300, Jan. 2020. DOI: 10.1016/j.engappai.2019.103300.
  • S. I. Selem, H. M. Hasanien, and A. A. El-Fergany, “Parameters extraction of PEMFC’s model using manta rays foraging optimizer,” Int. J. Energy Res., vol. 44, no. 6, pp. 4629–4640, Feb. 2020. DOI: 10.1002/er.5244.
  • A. D. Abdulkareem and S. R. Mohammed, “Comparison between two new censored regression models extended from Burr-XII system with application,” Int. J. Nonlinear Anal. Appl., vol. 13, pp. 3395–3403, 2022. DOI: 10.22075/ijnaa.2022.6100.
  • S. Lipirodjanapong, C. Suwanasri, T. Suwanasri, and W. Wangdee, “Empirical circuit breaker failure rate assessment and modelling in a preventive maintenance application,” Electr. Power Compon. Syst., vol. 43, no. 16, pp. 1832–1842, Aug. 2015. DOI: 10.1080/15325008.2015.1057780.
  • M. Govardhan and R. Roy, “Comparative analysis of economic viability with distributed energy resources on unit commitment,” Electr. Power Compon. Syst., vol. 44, no. 14, pp. 1588–1607, Aug. 2016. DOI: 10.1080/15325008.2016.1174907.
  • S. A. Akdağ and A. Dinler, “A new method to estimate Weibull parameters for wind energy applications,” Energy Convers. Manage., vol. 50, no. 7, pp. 1761–1766, Jul. 2009. DOI: 10.1016/j.enconman.2009.03.020.
  • L. Zhao, M. S. Nazir, H. M. J. Nazir, and A. N. Abdalla, “A review on proliferation of artificial intelligence in wind energy forecasting and instrumentation management,” Environ. Sci. Pollut. Res., vol. 29, no. 29, pp. 43690–43709, Jun. 2022. DOI: 10.1007/s11356-022-19902-8.

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