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DC Field | Value | Language |
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dc.contributor.author | Lázár István | hu |
dc.contributor.author | Hadnagy Istvan | en |
dc.contributor.author | Hadnagy István | hu |
dc.contributor.author | Гаднадь Іштван | uk |
dc.contributor.author | Boglárka Bertalan-Balázs | hu |
dc.contributor.author | Bertalan László | hu |
dc.contributor.author | Szegedi Sándor | hu |
dc.date.accessioned | 2024-11-26T11:39:17Z | - |
dc.date.available | 2024-11-26T11:39:17Z | - |
dc.date.issued | 2024 | - |
dc.identifier.citation | In Energy Conversion and Management: X. 2024. Volume 24. 11 p. | en |
dc.identifier.issn | 2590-1745 (Online) | - |
dc.identifier.other | DOI: https://doi.org/10.1016/j.ecmx.2024.100760 | - |
dc.identifier.uri | https://dspace.kmf.uz.ua/jspui/handle/123456789/4492 | - |
dc.description.abstract | Abstract. Exact knowledge of wind energy potential is a fundamental issue in wind energy utilization. The vertical changes in wind speeds, that is, the wind profile, have a predominant impact on the wind energy available at a location because the kinetic energy of moving air is proportional to the square of the wind speed. Roughness describes the resistance of a 3D surface to moving air. The exponent α of the power law of Hellmann and the roughness length (z0) are two parameters that describe the effects of the roughness of the surface on the wind profile. They can be used for the vertical extrapolation of wind speeds. The exponent α can be determined using multiple height level wind speed measurement data, whereas a reliable technique for the calculation of the roughness length requires detailed knowledge of the 3D geometry of the measurement site. In the present study, the exponent α was calculated based on SODAR wind speed measurements, while (z0) was determined using a combination of GIS and UAS-based aerial survey methods. Wind speeds measured at 50 m were extrapolated for height levels of 80, 90, 100, 110, and 120 m using dynamic power law exponent values. Wind power was determined using the power law (method V1), roughness length (method V2), frequency distribution (method W-RF), and gamma distribution (method W-G), and Windographer software was compared to the values calculated from the empirical (measured) wind speeds. A comparative statistical analysis of the datasets of the power law and roughness length methods on monthly/diurnal, annual/diurnal, and month/direction contexts showed no sig nificant differences at all height levels. Differences can be detected in the distribution of the signs of the dif ferences at heights of 80 and 120 m for the entire dataset. Underestimation was dominant with a significant frequency (over 70 %) in the case of both methods and heights. There were no significant differences between the wind power estimations provided by the different methods, and all the methods involved in the study under estimated the wind speeds and wind energy potential for each height level. Methods V1 and V2 can be used alternatively, depending on the input data available for analysis. The major advantage of method V2 is that it provides the same accuracy as V1, which requires a UAS-based aerial survey at the beginning, but continuous wind measurements must be performed at a lower height only. This means that there is no need for a high measurement tower, which makes the measurements simpler, more cost-effective, and causes much less disturbance to the environment. Another important advantage of the methods presented here is that they use a dynamic approach of power law exponent values that provide a more realistic estimation of wind speed and energy on a diurnal scale. | en |
dc.description.sponsorship | Project no. TKP2021-NKTA-34 was implemented with support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-NKTA funding scheme. During the preparation of this work, the author(s) used the Paperpal: AI Academic Writing Tool to improve the English of the manuscript. After using this tool/service, the authors reviewed and edited the content as required and took full responsibility for the content of the publication. | en |
dc.language.iso | en | en |
dc.publisher | Elsevier Ltd. | en |
dc.relation.ispartofseries | ;Volume 24. | - |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | Wind profile | en |
dc.subject | Remote sensing | en |
dc.subject | Power law | en |
dc.subject | Roughness length | en |
dc.subject | Weibull distribution | en |
dc.subject | Wind energy potential estimation | en |
dc.title | Comparative examinations of wind speed and energy extrapolation methods using remotely sensed data – A case study from Hungary | en |
dc.type | dc.type.researchStudy | en |
Appears in Collections: | Hadnagy István |
Files in This Item:
File | Description | Size | Format | |
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Hadnagy_I_et_al_Comparative_examinations_of_wind_speed_and_energy_extrapolation_2024.pdf | In Energy Conversion and Management: X. 2024. Volume 24. 11 p. | 1.3 MB | Adobe PDF | View/Open |
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