Statistical Analysis Of Wind Speed Distribution Of Turkey As Regional

Yıl 2018, Cilt: 3 Sayı: 1, 35 - 55, 30.04.2018

Ebru Akpınar , Sinan Akpınar , Nilay Balpetek

https://doi.org/10.30931/jetas.407141

Cited By: 3

Öz

In this study, the statistical analysis of wind power density and wind speed distribution parameters of the selected cities from seven region of Turkey was investigated using the hourly wind speed data measured by the Turkish State Meteorological Service between 2005 and 2014. The Weibull and Rayleigh distributions were used for modeling and the success of this modeling process was evaluated according to the parameters of R2, RMSE and c2. The Weibull parameters and the Rayleigh parameters were estimated analytically, and the mean wind speed and energy potential were determined based on these parameters. At the Weibull distribution, the lowest mean wind speed and power density was obtained as 1.73 m/s and 5.78 W/m2 in Adıyaman, respectively. The highest mean speed and power density was determined as 2.95 m/s and 33.32 W/m2 in Sinop. At the Rayleigh distribution, the lowest and the highest mean speed and the power density was obtained as 1.72 m/s and 5.63 W/m2 in Adıyaman, 3.06 m /s and 33.44 W/m2 in Sinop, respectively. Generally, the highest mean wind speed and power density values were determined in Sinop, and the lowest mean wind speed and power density values in Adıyaman. According to statistical criteria in wind data analysis of Turkey, the Weibull distribution was better than the Rayleigh distribution.

Anahtar Kelimeler

Wind energy, statistically analysis of the wind energy potential, Weibull distribution, Rayleigh distribution, Turkey

Kaynakça

• [1] Kaplan, Y.A., Aladag, C., “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) 2016 : 232-242.
• [2] Ilkılıc, C., “Wind Energy And Assessment of Wind Energy Potential in Turkey”. Renewable and Sustainable Energy Reviews 16 (2012) : 1165– 1173.
• [3] Azad, A.K., Rasula, M.G., Alam, M.M., Ameer, Uddinb S.M., Mondal, S.K., “Analysis of Wind Energy Conversion System Using Weibull Distribution”. Procedia Engineering 90 (2014): 725-732.
• [4] Dokur, E., Kurban, M., “Wind Speed Potential Analysis Based on Weibull Distribution”. Balkan Journal of Electrical & Computer Engineering 3(4) (2015) : 231-235.
• [5] Dokur, E., Kurban, M., Ceyhan, S., “Wind Speed Modelling Using Inverse Weibull Distrubition: A Case Study For Bilecik, Turkey”. International Journal of Energy Applications and Technologies 3(2) (2016) : 55–59.
• [6] İncecik, S., Erdoğmuş, F., “An Investigation of The Wind Power Potential on The Western Coast of Anatolia”. Renewable Energy 6 (1995) : 863-865.
• [7] Ulgen, K., Hepbasli, A., “Determination of Weibull Parameters for Wind Energy Analysis of Izmir, Turkey.” Int J Energy Res 26 (2002) : 494–506.
• [8] Celik, A.N., “A Statistical Analysis of Wind Power Density Based on the Weibull and Rayleigh Models at the Southern Region of Turkey.” Renewable Energy 29(4) (2003):593–604.
• [9] Karsli, V.M., Gecit, C., “An Investigation on Wind Power Potential of Nurdagı- Gaziantep, Turkey”. Renew Energy 28 (2003) : 823–830.
• [10] Kose, R., Ozgur, M.A., Erbas, O., Tugcu, A., “The Analysis of Wind Data and Energy Potential in Kutahya, Turkey”. Renew Sustain Energy Rev 8 (2004) : 277–88.
• [11] Akpinar, E.K., Akpinar, S., “Determination of the Wind Energy Potential for Maden-Elazığ, Turkey”. Energy Conversion and Management 45 (2004): 2901-2914.
• [12] Akpinar, E.K., Akpinar, S., “Statistical Analysis of Wind Energy Potential on The Basis of the Weibull And Rayleigh Distribution for Ağın-Elazığ Turkey”. J.Power Energy 218 (2004) : 557-565.
• [13] Akpinar, E.K., Akpinar, S., “A Statistical Analysis of Wind Speed Data Used In Installation of Wind Energy Conversion Systems”. Energy Conversion and Management 46(4) (2005) : 515-532.
• [14] Akpinar, E.K., “A Statistical Investigation of Wind Energy Potential”. Energy Sources, Part A 28 (2006): 807–820.
• [15] Genc, A., Erisoglu, M., Pekgor, A., Oturanc, G., Hepbasli, A., Ulgen, K., “Estimation of Wind Power Potential Using Weibull Distribution”. Energ Source 27 (2005) : 809-822.
• [16] Gökcek, M., Bayülken, A., Bekdemir, Ş., “Investigation of Wind Characteristics and Wind Energy Potential in Kirklareli, Turkey”. Renewable Energy 32 (2007) : 1739-1752.
• [17] Akdag, S.A., Dinler, A., “A New Method to Estimate Weibull Parameters for Wind Energy Applications”. Energ Convers Manage 50 (2009) : 1761-1766.
• [18] Mert, I., Karakus, C., “A Statistical Analysis of Wind Speed Data Using Burr, Generalized Gamma, and Weibull Distributions in Antakya, Turkey”. Turk J Elec Eng & Comp Sci 23 (2015) : 1571 -1586.
• [19] Lun, I.Y.F., Lam, J.C., “A Study of Weibull Parameters Using Long-Term Wind Observations”. Renewable Energy 20 (2000) : 145-153.