Rüzgar Hızı Değerlendirmesinde İki Parametreli Weibull Dağılımı İçin Üç Parametre Tahmin Metodunun Karşılaştırmalı Performans Analizi

Abstract

İki parametreli Weibull dağılımı rüzgâr hızı tahmininde yaygın bir şekilde kullanılmaktadır. Bu çalışmada, iki parametreli Weibull dağılımı için yakın zamanda önerilmiş belirli parametre tahmin metotları, Türkiye’nin iki ilinin rüzgâr verileri kullanılarak karşılaştırılmıştır. Bu çalışmada kullanılan metotlar; Rüzgâr Enerjisi Yoğunlaştırma metodu (WEIM), Enerji Örüntü Faktörü metodu (EPFM) ve Güç Yoğunluğu metodudur (PD). Bu metotların olasılık-yoğunluk grafiğindeki uyumları karşılaştırılmıştır. Ayrıca, bu metotların, rüzgâr enerjisini tahmin etmedeki performansları da karşılaştırılmıştır. Son olarak, belirtilen performans kriterleri kullanılarak bir karşılaştırma yapılmıştır; Rüzgâr Enerji Hatası (WEE), Ortalama Karekök Hatası (RMSE), Belirleme Katsayısı (R2). Sonuç olarak, EPFM ve PD metotları grafik üzerinde oldukça yakın bir uyum gösterirken, rüzgâr enerji yoğunluklarını hesaplamada PD metodu önemli bir fark göstermiştir. Ayrıca, hata performansları incelendikten sonra, istenen sonuçlara EPFM tarafından ulaşıldığı görülmüştür. Sonuç olarak, bu çalışmada istenen sonuca yakın değerler veren metodun Enerji Örüntü Faktörü metodu olduğu görülmüştür.

Keywords

• Rüzgâr enerjisi
• Parametre tahmini
• Weibull Dağılımı
• EPFM
• PD
• WEIM

Comparative Performance Analysis of Three Parameter Estimation Methods for Two Parameter Weibull Distribution in Wind Speed Assessment

Abstract

The two-parameter Weibull distribution is widely used in the estimation of wind speed. In this paper, some recently proposed parameter estimation methods for the two parameter Weibull distribution have been compared using the wind data from two cities in Turkey. The compared methods in this paper are Wind Energy Intensification Method (WEIM), Energy Pattern Factor Method (EPFM), and Power Density Method (PD). These methods have been compared by checking the fit have provided in the probability-density graph. The comparison has also been made by comparing of efficiencies these methods in predicting wind energy density. Lastly, a comparison has been investigated by using the following performance criteria; Wind Energy Error (WEE), Root Mean Square Error (RMSE), Coefficient of Determination (R2), and the results of error analysis have been compared. According to simulation results, while the EPFM and PD methods have shown a very close fit on the graph; when it comes to calculating wind energy densities, the PD method has shown a significant advantage over other methods. Finally, after the examination of error criteria, it was clear that EPFM has shown accurate performance which is the desired result. The main conclusion is that the accurate method for these selected regions is the EPFM.

Keywords

• Wind energy
• Parameter estimation
• Weibull distribution
• EPFM
• PD
• WEIM

References

1. Akdağ, S. A., & Dinler, A. (2009). “A new method to estimate Weibull parameters for wind energy applications.”, Energy Conversion and Management, 50(7), 1761-1766. doi:10.1016/j.enconman.2009.03.020
2. Akpinar, E. K., & Akpinar, S. (2005). “An assessment on seasonal analysis of wind energy characteristics and wind turbine characteristics”, Energy Conversion and Management, 46(11–12), 1848–1867.
3. Akyuz, H. E., & Gamgam, H. (2017). “Statistical analysis of wind speed data with weibull, lognormal and gamma distributions”, Cumhuriyet Science Journal, 68–76.
4. Alrashidi, M., Rahman, S., & Pipattanasomporn, M. (2020). “Metaheuristic optimization algorithms to estimate statistical distribution parameters for characterizing wind speeds”, Renewable Energy, 149, 664–681.
5. Bañuelos-Ruedas, F., Angeles-Camacho, C., & Rios-Marcuello, S. (2010). “Analysis and validation of the methodology used in the extrapolation of wind speed data at different heights”, Renewable and Sustainable Energy Reviews, 14(8), 2383–2391.
6. Chang, T. P. (2011). “Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application.”, Applied Energy, 88(1), 272-282. doi:10.1016/j.apenergy.2010.06.018
7. Chaurasiya, P. K., Ahmed, S., & Warudkar, V. (2018). “Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument”, Alexandria Engineering Journal, 57(4), 2299–2311.
8. Counihan, J. (1975). “Adiabatic atmospheric boundary layers: A review and analysis of data from the period 1880–1972”, Atmospheric Environment, 9(10), 871–905.

9. Dokur, E., & Kurban, M. (2015). Wind speed potential analysis based on Weibull distribution. Balkan Journal of Electrical and Computer Engineering, 3, 231-235.
10. Enerji Atlası. Retrieved January 13, 2021 from https://www.enerjiatlasi.com/ruzgar-enerjisi-haritasi/turkiye
11. Gualtieri, G., & Secci, S. (2011). “Comparing methods to calculate atmospheric stability-dependent wind speed profiles: A case study on coastal location”, Renewable Energy, 36(8), 2189–2204.
12. Kidmo Kaoga, D., Doka Yamigno, S., Raidandi, D., & Djongyang, N. (2014). “Performance analysis of Weibull methods for estimation of wind speed distributions in the adamaoua region of Cameroon”, International Journal of Basic and Applied Sciences, 3(3). doi:10.14419/ijbas.v3i3.3081
13. Kidmo, D. K., Danwe, R., Doka, S. Y., & Djongyang, N. (2015). “Statistical analysis of wind speed distribution based on six Weibull Methods for wind power evaluation in Garoua, Cameroon.”, Revue des Energies Renouvelables, 18(1), 105-125.
14. MERRA. (n.d.). Retrieved January 12, 2021, from http://www.soda-pro.com/web-services/meteo-data/merra
15. Sumair, M., Aized, T., Gardezi, S. A., Rehman, S. U., & Rehman, S. M. (2020). “A novel method developed to estimate Weibull parameters”, Energy Reports, 6, 1715-1733.
16. Usta, I., Arik, I., Yenilmez, I., & Kantar, Y. M. (2018). “A new estimation approach based on moments for estimating Weibull parameters in wind power applications”, Energy Conversion and Management, 164, 570–578.