Determination of Weibull parameters using the standard deviation method and performance comparison at different locations

Document Type : Article

Author

Energy Engineering Department of Osmaniye Korkut Ata University, Osmaniye, Turkey

Abstract

This study analyzed the harmony of the Weibull Distribution Function (WDF) and the real data obtained from three different locations. The Standard Deviation Method (SDM) was used to determine the coefficients of the WDF at Adana, Osmaniye and Hatay regions. One of the important purposes of this study is to observe how the performance of the SDM changes in regions with different mean wind speeds. The statistical fittings of the calculated and measured wind speed data were evaluated for justifying the performance of the SDM. The obtained results were compared with such error analyses as Coefficient of Determination (R2), Mean Percentage Error (MPE) and Root Mean Square Error (RMSE). Obtained data were examined on monthly, seasonal and annual bases. The power density is a major key issue for suitability use of wind energy. The calculated power densities of all selected regions were compared with wind power density derived from measured wind data. The performance of the method mentioned in this study was examined in detail at different regions with different geographic characteristics. For the selected three regions, the performance of the SDM was evaluated at different mean wind speeds varying over the years.

Keywords


  1. Kaplan, Y.A. Performance assessment of power density method for determining the weibull distribution coe_cients at three di_erent locations", Flow Measurement and Instrumentation, 63, pp. 8{13 (2018). 2. Chaurasiya, P.K., Ahmed, S., and Warudkar, V. Comparative analysis of Weibull parameters for wind data measured from met-mast and remote sensing techniques", Renewable Energy, 115, pp. 1153{1165 (2018). 3. Agalar, S. and Kaplan, Y.A. Design of a custom power park for wind turbine system and analysis of the system performance under power quality disturbances", IET Renewable Power Generation, 9(8), pp. 943{953 (2015). 4. C_ apika, M., Y_lmaz, A.O., and C_ avusoglu, I. Present situation and potential role of renewable energy in Turkey", Renewable Energy, 46, pp. 1{13 (2012). 5. Gabbasa, M., Sopian, K., Yaakob, Z., et al. Review of the energy supply status for sustainable development in the organization of islamicc conference", Renewable and Sustainable Energy Reviews, 28, pp. 18{28 (2013). 6. GWEC (Global Wind Energy Council), Global w_nd 2016 report (May 2017). 7. TEIAS. Turkish Electricity Transmission Company (2016). hhttp://www.teias.gov.tri. 8. Chaurasiya, P.K., Ahmed, S., and Warudkar, V. Study of di_erent parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument", Alexandria Engineering Journal, 57, pp. 2299{2311 (2018). 9. Soulouknga, M.H., Doka, S.Y., Revanna, N., et al. Analysis of wind speed data and wind energy potential in Faya-Largeau, Chad, using Weibull distribution", Renewable Energy, 121, pp. 1{8 (2018). 10. Katinas, V., Gecevicius, G., and Marciukaitis, M. An investigation of wind power density distribution at location with low and high wind speeds using statistical model", Applied Energy, 218, pp. 442{451 (2018). 11. Aries, N., Boudia, S.M., and Ounis, H. Deep assessment of wind speed distribution models: A case study of four sites in Algeria", Energy Conversion and Management, 155, pp. 78{90 (2018). 12. Usta, I., Arik, I., Yenilmez, I., and Kantar, Y.M. A new estimation approach based on moments for estimating Weibull parameters in wind power applications", Energy Conversion and Management, 164, pp. 570{578 (2018). 13. Shoaib, M., Siddiqui, I., Amir, Y.M., et al. Evaluation of wind power potential in Baburband (Pakistan) using weibull distribution function", Renewable and Sustainable Energy Reviews, 70, pp. 1343{1351 (2017). 14. Katinas, V., Mar_ciukaitis, M., Gecevi_cius, G., et al. Statistical analysis of wind characteristics based on Weibull methods for estimation of power generation in Lithuania", Renewable Energy, 113, pp. 190{201 (2017). 15. Aukitino, T., Khan, M.G., and Ahmed, M.R. Wind energy resource assessment for Kiribati with a comparison of di_erent methods of determining weibull parameters", Energy Conversion and Management, 151, pp. 641{660 (2017). 16. Freitas de Andrade, C., Maia Neto, H.F., Costa Rocha, P.A., et al. An e_ciency comparison of numerical methods for determining Weibull parameters for wind energy applications: A new approach applied to the northeast region of Brazil", Energy Convers Manage, 86(10), pp. 801{808 (2014). 17. Azad, A.K., Rasul, M.G., and Yusaf, T. Statistical diagnosis of the best weibull methods for wind power assessment for agricultural applications", Energies, 7, pp. 3056{3085 (2014). 18. Kaoga, D.K., Sergeb, D.Y., Raidandic, D., et al. Performance assessment of two-parameter weibull distribution methods for wind energy applications in the district of Maroua in Cameroon", International Journal of Sciences, Basic and Applied Research (IJSBAR), 17(1), pp. 39{59 (2014). 19. Kaplan, Y.A. Determination of the best weibull methods for wind power assessment in the southern region of Turkey", IET Renewable Power Generation, 11(1), pp. 175{182 (2017). Y.A. Kaplan/Scientia Iranica, Transactions D: Computer Science & ... 27 (2020) 3075{3083 3083 20. Kaplan, Y.A. Determination of Weibull parameters by di_erent numerical methods and analysis of wind power density in Osmaniye, Turkey", Scientia Iranica, 24(6), pp. 3204{3212 (2017). 21. Kantar, Y.M. and Usta, I. Analysis of wind speed distributions: wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function", Energy Convers Manage, 49, pp. 962{973 (2008). 22. Akda_g, S.A. and Dinler, A. A new method to estimate weibull parameters for wind energy applications", Energy Conversion and Management, 50(7), pp. 1761{ 1766 (2009). 23. Khan, J.K., Ahmed, F., Uddin, Z., et al. Determination of Weibull parameter by four numerical methods and prediction of wind speed in Jiwani (Balochistan)", Journal of Basic and Applied Sciences, 11, pp. 62{68 (2015). 24. Islam, M.R., Saidur, R., and Rahim, N.A. Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function", Energy, 36(2), pp. 985{992 (2011). 25. Chang, T.P. Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application", Appl Energy, 88, pp. 272{282 (2011). 26. Morgan, E.C., Lackner, M., Vogal, R.M., et al. Probability distributions of o_shore wind speeds", Energy Conversion and Management, 52, pp. 15{26 (2011). 27. Mohammadi, K. and Mostafaeipour, A. Using di_erent methods for comprehensive study of wind turbine utilization in Zarrineh, Iran", Energy Conversion and Management, 65, pp. 463{470 (2013). 28. Gokcek, M., Bayulken, A., and Bekdemir, S. Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey", Renewable Energy, 32, pp. 1739{1752 (2007). 29. Talha, A., Bulut, Y.M., and Yavuz, A. Comparative study of numerical methods for determining Weibull parameters for wind energy potential", Renewable and Sustainable Energy Reviews, 40, pp. 820{825 (2014). 30. Usta, I. An innovative estimation method regarding Weibull parameters for wind energy applications", Energy, 106, pp. 301{314 (2016).