STATISTICAL ANALYSIS OF WIND SPEED DISTRIBUTION WITH SINOP-TURKEY APPLICATION

In this study, the wind energy potential of the Sinop region was analyzed statistically by using the Turkish State Meteorological Station’s hourly wind speed data between the years of 2005-2014. The two- parameter Weibull and one-parameter Rayleigh probability distribution functions were used to determine the wind energy potential of the region. The probability distribution functions were derived from the cumulative function and used to calculate the mean wind speed and the variance of the actual data. The best way of representing the performance of the Weibull and Rayleigh distributions is to use the statistical parameters such as the correlation coefficient (R2), chi-square (χ2) and the root mean square error analysis (RMSE). The results of the study showed that Sinop has a mean wind speed of 3.36 m/s with a maximum value of 4.28 m/s in February of 2011, and a minimum value of 2.41 m/s in March of 2013, while the corresponding mean wind power density is approximately 33.31 W/m2 for the whole year. In general, it was determined the wind speed is higher during some winter and spring months, notably February and March, and is lower during the autumn months. The Weibull distribution function was found to be more appropriate than the Rayleigh distribution function.

Keywords:

Wind Energy, Statistical Analysis of Wind Energy Potential, Weibull Distribution, Rayleigh Distribution Sinop-Turkey,

PDF

___

[1] Pishgar-Komleh, S.H., Keyhani, A., Sefeedpari, P. (2015). Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran). Renewable and Sustainable Energy Reviews, 42, 313–322.
[2] Sohoni, V., Gupta, S., Nema, R. (2016). A comparative analysis of wind speed probability distributions for wind power assessment of four sites. Turk J Elec Eng & Comp Sci, 24, 4724-4735.
[3] Morgan, V.T. (1995). Statistical distributions of wind parameters at Sydney, Australia. Renew Energy, 6, 39-47.
[4] Seguro, J.V., Lambert, T.W. (2000). Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. J Wind Eng Ind Aerod, 85, 75-84.
[5] Costa Rocha, P.A., De Sousa, R.C., De Andrade, C.F., Da Silva, M.E.V. (2012). Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil. Appl Energ, 89, 395-400.
[6] Ulgen, K., Hepbasli, A. (2002). Determination of Weibull parameters for wind energy analysis of Izmir, Turkey. Int J Energy Res, 26, 494–506.
[7] Celik, A.N. (2003). A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey. Renewable Energy, 29(4), 593–604.
[8] Karsli, V.M., Gecit, C. (2003). An investigation on wind power potential of Nurdagı- Gaziantep, Turkey. Renew Energy, 28, 823–830.
[9] Kose, R., Ozgur, M. A., Erbas, O., Tugcu, A. (2004). The analysis of wind data and energy potential in Kutahya, Turkey. Renew Sustain Energy Rev, 8, 277–288.
[10] Akpinar, E.K., Akpinar, S. (2004). Determination of the wind energy potential for Maden-Elazığ, Turkey. Energy Conversion and Management, 45, 2901-2914.
[11] Akpinar, E.K., Akpinar, S. (2004). Statistical Analysis of wind energy potential on the basis of the Weibull and Rayleigh distribution for Ağın-Elazığ, Turkey. J.Power Energy, 218, 557-565.
[12] Genc, A., Erisoglu, M., Pekgor, A., Oturanc, G., Hepbasli, A., Ulgen, K. (2005). Estimation of wind power potential using Weibull distribution. Energ Source, 27, 809-822.
[13] Akpinar, E.K. (2006). A statistical investigation of wind energy potential. Energy Sources, Part A, 28, 807–820.
[14] Gökcek, M., Bayülken, A., Bekdemir, Ş. (2007). Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey. Renewable Energy, 32, 1739-1752.
[15] Yilmaz, V., Çelik, H.E. (2008). A statistical approach to estimate the wind speed distribution: the case of Gelibolu region. Doğuş Üniversitesi Dergisi, 9 (1), 122-132.
[16] Akdag, S.A., Güler, Ö. (2009). Calculation of wind energy potential and economic analysis by using Weibull Distribution—A case study from Turkey. Part 1: Determination of Weibull parameters. Energy Sources, Part B, 4, 1–8.
[17] Bilgili, M. and Şahin, B. (2009). Investigation of wind energy density in the Southern and Southwestern region of Turkey. Journal of Energy Engineering, 135, 1(12), 12-20.
[18] Mert, I., Karakus, C. (2015). A statistical analysis of wind speed data using Burr, generalized gamma, and Weibull distributions in Antakya, Turkey. Turk J Elec Eng & Comp Sci, 23, 1571 -1586.
[19] Dokur, E., Kurban, M. (2015). Wind speed potential analysis based on Weibull distribution. Balkan Journal of Electrical & Computer Engineering, 3(4), 231-235.
[20] Kaplan, Y.A., Aladağ, C. (2016). Comparison of different methods in estimating Weibull distribution parameters for wind power application. International Journal of Innovative Research in Science, Engineering and Technology. 5(12), 232-242.
[21] Yanıktepe, B., Özalp, C., Kaşka, Ö., Köroğlu T. (2011). An assessment of wind power potential in Osmaniye, Turkey. 6th International Advanced Technologies Symposium (IATS’11), 16-18 May 2011, Elazığ, Turkey, 82-88.
[22] https://tr.depositphotos.com/vector-images/marmara.html?qview=53816051.

Başlangıç: 2015
Yayıncı: YILDIZ TEKNİK ÜNİVERSİTESİ

Arşiv