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


References
1. Kaplan, Y.A. Performance assessment of power density
method for determining the weibull distribution
coecients 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., Ylmaz, 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 wnd
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., Marciukaitis, M., Gecevicius, 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 eciency 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. Akdag, 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).