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

Yıl 2024, Cilt: 13 Sayı: 4, 5 - 10, 30.12.2024
https://doi.org/10.46810/tdfd.1440444

Öz

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.

Proje Numarası

FBA-2018-6855

Kaynakça

  • 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.
  • 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.
  • Fyrippis, I., Axaopoulos, P. J., Panayiotou, G. Wind energy potential assessment in Naxos Island, Greece. Applied Energy, 2010; 87(2), 577-586.
  • 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.
  • 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.
  • Mabel, M. C., Fernandez, E. Growth and future trends of wind energy in India. Renewable and Sustainable Energy Reviews, 2008;12(6), 1745-1757.
  • 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.
  • 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.
  • 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.
  • Kusiak, A., Zheng, H., Song, Z. On-line monitoring of power curves. Renewable Energy, 2009; 34(6), 1487-1493.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • Raghunathan, K., Subramaniam, V., Srinivasamoorthy, V. R. Studies on the tensile characteristics of ring and rotor yarns using modified Weibull distribution, 2002.
  • 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.
  • 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.
  • Aksoy, H. Use of gamma distribution in hydrological analysis. Turkish Journal of Engineering and Environmental Sciences, 2000; 24(6), 419-428.
  • Shapiro, S. S., Chen, L. Composite tests for the gamma distribution. Journal of Quality Technology, 2001;33(1), 47-59.
  • Hristopulos, D. T., Petrakis, M. P., Kaniadakis, G. Weakest-link scaling and extreme events in finite-sized systems. Entropy, 2015;17(3), 1103-1122.
  • Johnson, N. L., Kotz, S., Balakrishnan, N. Continuous univariate distributions, volume 2 (Vol. 289). John wiley & sons;1995.
  • Burr, I. W. Cumulative frequency functions. The Annals of mathematical statistics, 1942; 13(2), 215-232.
  • 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.
  • Mudholkar, G. S., Srivastava, D. K. Exponentiated Weibull family for analyzing bathtub failure-rate data. IEEE transactions on reliability, 1993;42(2), 299-302.
  • 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.

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

Yıl 2024, Cilt: 13 Sayı: 4, 5 - 10, 30.12.2024
https://doi.org/10.46810/tdfd.1440444

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.

Etik Beyan

Çalışmamız için Etik Kurul Belgesine İhtiyaç Yoktur

Destekleyen Kurum

Van Yüzüncü Yıl Üniversitesi

Proje Numarası

FBA-2018-6855

Teşekkür

Van yüzüncü Yıl ünibersitesi bilimsel araştırma ve proje kordinatörlüğüne "FBA-2018-6855" numaralı projemizi desteklediği için teşekkür ederiz.

Kaynakça

  • 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.
  • 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.
  • Fyrippis, I., Axaopoulos, P. J., Panayiotou, G. Wind energy potential assessment in Naxos Island, Greece. Applied Energy, 2010; 87(2), 577-586.
  • 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.
  • 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.
  • Mabel, M. C., Fernandez, E. Growth and future trends of wind energy in India. Renewable and Sustainable Energy Reviews, 2008;12(6), 1745-1757.
  • 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.
  • 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.
  • 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.
  • Kusiak, A., Zheng, H., Song, Z. On-line monitoring of power curves. Renewable Energy, 2009; 34(6), 1487-1493.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • Raghunathan, K., Subramaniam, V., Srinivasamoorthy, V. R. Studies on the tensile characteristics of ring and rotor yarns using modified Weibull distribution, 2002.
  • 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.
  • 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.
  • Aksoy, H. Use of gamma distribution in hydrological analysis. Turkish Journal of Engineering and Environmental Sciences, 2000; 24(6), 419-428.
  • Shapiro, S. S., Chen, L. Composite tests for the gamma distribution. Journal of Quality Technology, 2001;33(1), 47-59.
  • Hristopulos, D. T., Petrakis, M. P., Kaniadakis, G. Weakest-link scaling and extreme events in finite-sized systems. Entropy, 2015;17(3), 1103-1122.
  • Johnson, N. L., Kotz, S., Balakrishnan, N. Continuous univariate distributions, volume 2 (Vol. 289). John wiley & sons;1995.
  • Burr, I. W. Cumulative frequency functions. The Annals of mathematical statistics, 1942; 13(2), 215-232.
  • 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.
  • Mudholkar, G. S., Srivastava, D. K. Exponentiated Weibull family for analyzing bathtub failure-rate data. IEEE transactions on reliability, 1993;42(2), 299-302.
  • 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.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Enerjisi Üretimi (Yenilenebilir Kaynaklar Dahil, Fotovoltaikler Hariç)
Bölüm Makaleler
Yazarlar

Asuman Yılmaz 0000-0002-8653-6900

Mahmut Kara 0000-0001-7678-8824

Proje Numarası FBA-2018-6855
Yayımlanma Tarihi 30 Aralık 2024
Gönderilme Tarihi 21 Şubat 2024
Kabul Tarihi 19 Ekim 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 13 Sayı: 4

Kaynak Göster

APA Yılmaz, A., & Kara, M. (2024). Statistical Analysis of Wind Speed Data with Different Distributions: Bitlis, Türkiye. Türk Doğa Ve Fen Dergisi, 13(4), 5-10. https://doi.org/10.46810/tdfd.1440444
AMA Yılmaz A, Kara M. Statistical Analysis of Wind Speed Data with Different Distributions: Bitlis, Türkiye. TDFD. Aralık 2024;13(4):5-10. doi:10.46810/tdfd.1440444
Chicago Yılmaz, Asuman, ve Mahmut Kara. “Statistical Analysis of Wind Speed Data With Different Distributions: Bitlis, Türkiye”. Türk Doğa Ve Fen Dergisi 13, sy. 4 (Aralık 2024): 5-10. https://doi.org/10.46810/tdfd.1440444.
EndNote Yılmaz A, Kara M (01 Aralık 2024) Statistical Analysis of Wind Speed Data with Different Distributions: Bitlis, Türkiye. Türk Doğa ve Fen Dergisi 13 4 5–10.
IEEE A. Yılmaz ve M. Kara, “Statistical Analysis of Wind Speed Data with Different Distributions: Bitlis, Türkiye”, TDFD, c. 13, sy. 4, ss. 5–10, 2024, doi: 10.46810/tdfd.1440444.
ISNAD Yılmaz, Asuman - Kara, Mahmut. “Statistical Analysis of Wind Speed Data With Different Distributions: Bitlis, Türkiye”. Türk Doğa ve Fen Dergisi 13/4 (Aralık 2024), 5-10. https://doi.org/10.46810/tdfd.1440444.
JAMA Yılmaz A, Kara M. Statistical Analysis of Wind Speed Data with Different Distributions: Bitlis, Türkiye. TDFD. 2024;13:5–10.
MLA Yılmaz, Asuman ve Mahmut Kara. “Statistical Analysis of Wind Speed Data With Different Distributions: Bitlis, Türkiye”. Türk Doğa Ve Fen Dergisi, c. 13, sy. 4, 2024, ss. 5-10, doi:10.46810/tdfd.1440444.
Vancouver Yılmaz A, Kara M. Statistical Analysis of Wind Speed Data with Different Distributions: Bitlis, Türkiye. TDFD. 2024;13(4):5-10.