Statistical analysis of Wind Characteristic and Wind Energy Potential Based on Weibull Distribution in Bingol Province, Turkey

Öz

In this study, the statistical analysis of wind energy density and wind speed distribution parameters in Bingol province was examined using hourly wind speed data measured by the General Directorate of Meteorology between 2014 and 2017. Weibull distribution was used for statistical modeling and k and c parameters were calculated for 10 m and 30 m height. According to statistical criteria, in the wind data analysis of Bingol province, it was determined that the months with the highest potential in terms of mean wind speed and wind power densities are March, April and May. In the months when mean wind speeds are the highest, the dominant wind direction is south. As a result, it is concluded that since the average monthly and annual power densities in Bingol province are about 100 W/m2. It is determined that the wind potential of the region can be used for small scale off-grid wind applications. The fact that the average speed is mostly higher than 4 m/s for 30 m hub height has shown that electrical energy generation from wind energy is promising.

Anahtar Kelimeler

• Renewable energy
• Wind Characteristic
• Wind Energy Potential
• Weibull Distribution
• Wind frequency distribution

Kaynakça

1. 1] A. Kareem and Y. Tamura, Advanced structural wind engineering. Springer, 2013.
2. [2] M. Monfared, H. Rastegar, and H. Kojabadi, ‘‘A new strategy for wind speed forecasting using artificial intelligent methods,’’ Renewable Energy, vol. 34, no. 3, pp. 845–848, 2009.
3. [3] J. Chadee and C. Sharma, ‘‘Wind speed distributions: a new catalogue of defined models,’’ Wind Engineering, vol. 25, no. 6, pp. 319–337, 2001.
4. [4] P. Wais, ‘‘A review of weibull functions in wind sector,’’ Renewable and Sustainable Energy Reviews, vol. 70, pp. 1099–1107, 2017.
5. [5] H. Li and F. Zhang, ‘‘Summary on wind speed distribution and its parameter estimation,’’ Proceedings Advanced Materials Research, pp. 689–693.
6. [6] M. Sedghi, S. Hannani, and M. Boroushaki, ‘‘Estimation of weibull parameters for wind energy application in iran’s cities,’’ Wind Structures, vol. 21, no. 2, pp. 203–221, 2015.
7. [7] C. Seshaiah and K. Sukkiramathi, ‘‘A mathematical model to estimate the wind power using three parameter weibull distribution,’’ Wind Structures, vol. 22, no. 4, pp. 393–408, 2016.
8. [8] K. Ulgen and A. Hepbasli, ‘‘Determination of weibull parameters for wind energy analysis of izmir, turkey,’’ International Journal of Energy Research, vol. 26, no. 6, pp. 495–506, 2002.

9. [9] A. Celik, ‘‘A statistical analysis of wind power density based on the weibull and rayleigh models at the southern region of turkey,’’ Renewable Energy, vol. 29, no. 4, pp. 593–604, 2004.
10. [10] E. K. Akpinar and S. Akpinar, ‘‘Determination of the wind energy potential for maden-elazig, turkey,’’ Energy Conversion Management, vol. 45, no. 18-19, pp. 2901–2914, 2004.
11. [11] E. Akpinar and N. Balpetek, ‘‘Statistical analysis of wind energy potential of elazig province according to weibull and rayleigh distributions,’’ Journal of the Faculty of Engineering Architecture of Gazi University, vol. 34, no. 1, pp. 569–580, 2019.
12. [12] F. Mahmood, A. Resen, and A. Khamees, ‘‘Wind characteristic analysis based on weibull distribution of al-salman site, iraq,’’ Energy Reports, vol. 6, pp. 79–87, 2020.
13. [13] B. Safari and J. Gasore, ‘‘A statistical investigation of wind characteristics and wind energy potential based on the weibull and rayleigh models in rwanda,’’ Renewable Energy, vol. 35, no. 12, pp. 2874–2880, 2010.
14. [14] R. Mouangue, M. Kazet, A. Kuitche, and J.-M. Ndjaka, ‘‘Influence of the determination methods of k and c parameters on the ability of weibull distribution to suitably estimate wind potential and electric energy,’’ International Journal of Renewable Energy, vol. 3, no. 2, 2014.
15. [15] Y. Kaplan, ‘‘The evaluating of wind energy potential of osmaniye region with using weibull and rayleigh distributions,’’ Süleyman Demirel University Journal of Natural Applied Sciences, vol. 20, no. 1, pp. 62–71, 2016.
16. [16] O. Alavi, A. Sedaghat, and A.Mostafaeipour, ‘‘Sensitivity analysis of different wind speed distribution models with actual and truncated wind data: A case study for kerman, iran,’’ Energy Conversion and Management, vol. 120, pp. 51–61, 2016.
17. [17] J.Wang, X. Huang, Q. Li, and X. Ma, ‘‘Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of china,’’ Energy, vol. 164, pp. 432–448, 2018.
18. [18] S. Ali, S. Lee, and C. Jang, ‘‘Statistical analysis of wind characteristics using weibull and rayleigh distributions in deokjeok-do island - incheon, south korea,’’ Renewable Energy, vol. 123, pp. 652–663, 2018.
19. [19] T. Burton, D. Sharpe, N. Jenkins, and E. Bossanyi, Wind energy handbook. Wiley Online Library, 2001.
20. [20] J. Seguro and T. Lambert, ‘‘Modern estimation of the parameters of the weibull wind speed distribution for wind energy analysis,’’ Journal of Wind Engineering and Industrial Aerodynamics, vol. 85, no. 1, pp. 75–84, 2000.
21. [21] S. Kwon, ‘‘Uncertainty analysis of wind energy potential assessment,’’ Applied Energy, vol. 87, no. 3, pp. 856–865, 2010.
22. [22] S. Mathew and K. Pandey, ‘‘Analysis of wind regimes for energy estimation,’’ Renewable Energy, vol. 25, no. 3, pp. 381–399, 2002.
23. [23] K. Doğanşahin, A. Uslu, and B. Kekezoğlu, ‘‘İki bileşenli weibull dağılımı ile rüzgâr hızı olasılık dağılımlarının modellenmesi,’’ Avrupa Bilim ve Teknoloji Dergisi, vol. 15, pp. 315–326, 2019.
24. [24] A. Gungor, M. Gokcek, H. Ucar, E. Arabaci, and A. Akyuz, ‘‘Analysis of wind energy potential and weibull parameter estimation methods: a case study from turkey,’’ International Journal of Environmental Science and Technology, vol. 17, no. 2, pp. 1011–1020, 2020.
25. [25] Y. Kaplan, ‘‘Overview of wind energy in the world and assessment of current wind energy policies in turkey,’’ Renewable Sustainable Energy Reviews, vol. 43, pp. 562–568, 2015.
26. [26] P. Ramirez and J. Carta, ‘‘Influence of the data sampling interval in the estimation of the parameters of the weibull wind speed probability density distribution: a case study,’’ Energy Conversion and Management, vol. 46, no. 15-16, pp. 2419–2438, 2005.
27. [27] D. Weisser, ‘‘A wind energy analysis of grenada: an estimation using the ’weibull’ density function,’’ Renewable Energy, vol. 28, no. 11, pp. 1803–1812, 2003.
28. [28] T. Chang, ‘‘Performance comparison of six numerical methods in estimating weibull parameters for wind energy application,’’ Applied Energy, vol. 88, no. 1, pp. 272–282, 2011.
29. [29] M. Patel, Wind and solar power systems: design, analysis, and operation. CRC Press, Taylor and Francis Group, FL, USA, 2005.
30. [30] M. Baloch, S. Abro, G. Kaloi, N. Mirjat, S. Tahir, M. Nadeem, M. Gul, Z. Memon, and M. Kumar, ‘‘A research on electricity generation from wind corridors of pakistan (two provinces): A technical proposal for remote zones,’’ Sustainability-Basel, vol. 9, no. 9, 2017.
31. [31] G. Nage, ‘‘Analysis of wind speed distribution: Comparative study of weibull to rayleigh probability density function; a case of two sites in ethiopia,’’ American Journal of Modern Energy, vol. 2, no. 3, pp. 10–16, 2016.
32. [32] E. Dokur and M. Kurban, ‘‘Wind speed potential analysis based on weibull distribution,’’ Balkan Journal of Electrical Computer Engineering, vol. 3, pp. 231–235, 2015.
33. [33] O. Oral, M. İnce, B. Aylak, and M. Özdemir, ‘‘Estimation of wind speed probability distribution parameters by using four different metaheuristic algorithms,’’ ECJSE, vol. 9, no. 4, pp. 1342–1362, 2022.
34. [34] Y. Alcan, M. Demir, and S. Duman, ‘‘Sinop İlinin güneş enerjisinden elektrik Üretim potansiyelinin Ülkemiz ve almanya İle karşılaştırarak İncelenmesi,’’ ECJSE, vol. 5, no. 1, pp. 35–44, 2018.
35. [35] F. Tan and S. Shojaei, ‘‘Past to present: Solar chimney power technologies,’’ ECJSE, vol. 6, no. 1, pp. 220–235, 2019. 18 ECJSE Volume