RÜZGAR HIZI VERİLERİNİN FARKLI DAĞILIMLARA GÖRE İSTATİSTİKSEL ANALİZİ: BİTLİS, TÜRKİYE

Öz

Rüzgar hızının doğru bir şekilde modellenmesi, belirli bir bölgenin rüzgar enerjisi potansiyelinin tahmin edilmesi açısından önemlidir. İki parametreli Weibull dağılımı enerji literatüründe en yaygın kullanılan ve kabul edilen dağılımdır. Ancak doğada karşılaşılan tüm rüzgar hızı verilerini modellemez. Bu nedenle bu çalışmada rüzgâr enerjisinin modellenmesinde Gamma, log-normal, Genelleştirilmiş Rayleigh gibi farklı dağılımlar kullanılmıştır. Bu dağılımların bilinmeyen parametrelerinin tahmin edicileri, maksimum olabilirlik tahmin edicileri kullanılarak bulunur.

Anahtar Kelimeler

• Rüzgar hızı verileri, Weibull dağılımı, Gamma dağılımı, lognormal dağılımı, genelleştrilmiş Rayleigh dağılımı, en çok olabilirlik tahmini

Statistical Analysis of Wind Speed Data with Different Distributions: Bitlis, Türkiye

Abstract

Accurately modeling wind speed is important in estimating the wind energy potential of a specified region. Two- parameter Weibull distribution is the most widely used and accepted distribution in the energy literature. However, it does not model the all wind speed data encountered in nature. Therefore, in this study, different distributions are used for modeling wind energy, such as Gamma, lognormal, Generalized Rayleigh. The estimators of the unknown parameters of these distributions are found by using maximum likelihood estimators.

Keywords

• Wind speed data
• Weibull distribution
• lognormal distribution
• Gamma distribution
• generalized Rayleigh
• distribution
• maximum likelihood estimation

Kaynakça

1. Keyhani, A., Ghasemi-Varnamkhasti, M., Khanali, M., Abbaszadeh, R. An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran. Energy,2010; 35(1), 188-201.
2. Akpinar, E. K., Akpinar, S. An assessment on seasonal analysis of wind energy characteristics and wind turbine characteristics. Energy conversion and management, 2005; 46(11-12), 1848-1867.
3. Fyrippis, I., Axaopoulos, P. J., Panayiotou, G. Wind energy potential assessment in Naxos Island, Greece. Applied Energy, 2010; 87(2), 577-586.
4. Köse, R. An evaluation of wind energy potential as a power generation source in Kütahya, Turkey. Energy conversion and management, 2004; 45(11-12), 1631-1641.
5. Kaplan, Y. A. Overview of wind energy in the world and assessment of current wind energy policies in Turkey. Renewable and Sustainable Energy Reviews, 2015; 43, 562-568.
6. Mabel, M. C., Fernandez, E. Growth and future trends of wind energy in India. Renewable and Sustainable Energy Reviews, 2008;12(6), 1745-1757.
7. Mohammadi, K., Alavi, O., Mostafaeipour, A., Goudarzi, N., & Jalilvand, M. Assessing different parameters estimation methods of Weibull distribution to compute wind power density. Energy Conversion and Management, 2016; 108, 322-335.
8. Akgül, F. G., Şenoğlu, B., Arslan, T.. An alternative distribution to Weibull for modeling the wind speed data: Inverse Weibull distribution. Energy Conversion and Management, 2016; 114, 234-240.

9. Akdağ, S. A., Dinler, A. A new method to estimate Weibull parameters for wind energy applications. Energy conversion and management, 2009; 50(7), 1761-1766.
10. Kusiak, A., Zheng, H., Song, Z. On-line monitoring of power curves. Renewable Energy, 2009; 34(6), 1487-1493.
11. Brano, V. L., Orioli, A., Ciulla, G., Culotta, S. Quality of wind speed fitting distributions for the urban area of Palermo, Italy. Renewable Energy, 2011; 36(3), 1026-1039.
12. Carta, J. A., Ramírez, P., Velázquez, S. Influence of the level of fit of a density probability function to wind-speed data on the WECS mean power output estimation. Energy Conversion and Management, 2008; 49(10), 2647-2655.
13. Morgan, E. C., Lackner, M., Vogel, R. M., Baise, L. G. Probability distributions for offshore wind speeds. Energy Conversion and Management, 2011; 52(1), 15-26.
14. Sohoni, V., Gupta, S., Nema, R. A comparative analysis of wind speed probability distributions for wind power assessment of four sites. Turkish Journal of Electrical Engineering and Computer Sciences, 2016; 24(6), 4724-4735.
15. Bagci, K., Arslan, T., Celik, H. E. Inverted Kumarswamy distribution for modeling the wind speed data: Lake Van, Turkey. Renewable and Sustainable Energy Reviews, 2021;135, 110110
16. Raghunathan, K., Subramaniam, V., Srinivasamoorthy, V. R. Studies on the tensile characteristics of ring and rotor yarns using modified Weibull distribution, 2002.
17. Chiang, Y. J., Shih, C. D., Lin, C. C., Tseng, Y. Y. Examination of tyre rubber cure by Weibull distribution functions. International Journal of Materials and Product Technology, 2004; 20(1-3), 210-219.
18. Wood, M. A., Gunderson, B., Xia, A., Zhou, X., Padmanabhan, V., Ellenbogen, K. A. Temporal patterns of ventricular tachyarrhythmia recurrences follow a Weibull distribution. Journal of cardiovascular electrophysiology, 2005;16(2), 181-185.
19. Aksoy, H. Use of gamma distribution in hydrological analysis. Turkish Journal of Engineering and Environmental Sciences, 2000; 24(6), 419-428.
20. Shapiro, S. S., Chen, L. Composite tests for the gamma distribution. Journal of Quality Technology, 2001;33(1), 47-59.
21. Hristopulos, D. T., Petrakis, M. P., Kaniadakis, G. Weakest-link scaling and extreme events in finite-sized systems. Entropy, 2015;17(3), 1103-1122.
22. Johnson, N. L., Kotz, S., Balakrishnan, N. Continuous univariate distributions, volume 2 (Vol. 289). John wiley & sons;1995.
23. Burr, I. W. Cumulative frequency functions. The Annals of mathematical statistics, 1942; 13(2), 215-232.
24. Surles, J. G., Padgett, W. J. Inference for reliability and stress-strength for a scaled Burr type X distribution. Lifetime data analysis, 2001; 7, 187-200.
25. Mudholkar, G. S., Srivastava, D. K. Exponentiated Weibull family for analyzing bathtub failure-rate data. IEEE transactions on reliability, 1993;42(2), 299-302.
26. Esemen, M., Gürler, S. Parameter estimation of generalized Rayleigh distribution based on ranked set sample. Journal of Statistical Computation and Simulation, 2018; 88(4), 615-628.