Statistical analysis of measured wind speed data’s appealing spreadsheet applications

Cristian Paul CHIONCEL; Nicoleta GILLICH; Gelu-Ovidiu TIRIAN

doi:10.24193/subbeng.2021.1.10

Authors

Cristian Paul CHIONCEL Babeș-Bolyai University, Faculty of Engineering, Piaţa Traian Vuia, nr. 1-4, 320085, Reşiţa, Romania, cristian.chioncel@ubbcluj.ro https://orcid.org/0000-0002-9784-5960
Nicoleta GILLICH Babeș-Bolyai University, Faculty of Engineering, Piaţa Traian Vuia, nr. 1-4, 320085, Reşiţa, Romania, nicoleta.gillich@ubbcluj.ro (*corresponding author) https://orcid.org/0000-0001-5445-3468
Gelu-Ovidiu TIRIAN Politehnica University Timisoara, Faculty of Engineering, Str. Revoluţiei nr. 5, 331128, Hunedoara, Romania, ovidiu.tirian@fih.upt.ro

DOI:

https://doi.org/10.24193/subbeng.2021.1.10

Keywords:

wind energy, statistical analysis, Excel, wind data

Abstract

Once the wind data is measured, the values are processed, based on statistic approach, as accurately as possible, to provide a clear over-view of the locations wind potential, being the basis of any wind farm project, representing the go or no-go in further subsequent design steps. The probability density distributions are derived from time-series data, identifying the associated distributional parameters. The wind energy potential of the locations is studied based on the Rayleigh and Weibull models, implemented with the help of Excel computations, and representing tools, to understand the wind characteristics. Based on the statistical analysis of wind conditions presented here, the results of current study can be used to make a sustainable energy yield for any location.

References

Intergovernmental Panel on Climate Change – Climate Change 2021 Report, https://www.ipcc.ch/report/ar6/wg1/ (downloaded at September, 2021).

A European Green Deal, https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en (access August, 2021)

The significant role of wind energy, https://climatechange-theneweconomy.com/cop23-series-wind-turbines/, July, 2018.

Chioncel C.P., Chioncel P., Gillich N., Overview of Classic and Modern Wind Measurement Techniques, Basis of Wind Project Development, An-alele Universitatii ”Eftimie Murgu” Resita, Fascicula de Inginerie, 18(3), 2011, pp. 73-80.

Kidmo D.K., Danwe R., Doka S.Y., Djongyang N., Statistical analysis of wind speed distribution based on six Weibull Methods for wind power evaluation in Garoua, Cameroon, Revue des Energies Renouvelables,18, N°1 (2015), pp. 105–125.

Chioncel C.P., Dordescu M., Lazar M.A., Tirian G.-O., Wind turbine power and optimum energy, at variable wind speeds, IEEE 24th Interna-tional Conference on Intelligent Engineering Systems, July 8-10, 2020, Reykjavík, Iceland.

Ayodele T.R., Jimoh A.A., Munda J.L, Agee j.T., Statistical analysis of wind speed and wind power potential of Port Elizabeth using Weibull parameters, Journal of Energy in Southern Africa, 23(2), May 2012, pp. 30-38.

Extreme Wind Speeds Software: Excel, https://www.itl.nist.gov/div898/winds/excel.htm

Chioncel C.P., Gillich N., Spunei E., Tirian G.-O., Overview of the main topics in wind energy systems planning, IOP Conf. Series: Materials Science and Engineering 477, 2019, 012059.

Kwamboka J.O., Kamau J.N., Saoke C.O., Analysis of the Wind Energy Characteristics and Potential on the Hilly Terrain of Manga, Nyamira County, Kenya, International Journal of Innovative Science and Research Technology, 3(3), 2018, pp. 672-677.

Jung C., Schindler D., The role of air density in wind energy assessment – A case study from Germany, Energy, Volume 171(C), 2019, pp. 385-392.

Susan Stewart, Wind Turbine Systems, Department of Aerospace Engineering http://www.aero.psu.edu

Zaheer U., Muhammad A., D.A. Khan, Teaching physics using Microsoft Excel, IOP Conf. Series: Physics Education, 52(5), 2017.

https://support.microsoft.com/en-us/office/weibull-dist-function