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master's thesis
Statistička analiza vjetra i analiza proizvodnje buduće vjetroelektrane na zadanoj lokaciji
2019. urn:nbn:hr:200:142750
Prka, Marko
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Power Engineering
Chair of Power Systems and Substations

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Prka, M. (2019). Statistička analiza vjetra i analiza proizvodnje buduće vjetroelektrane na zadanoj lokaciji (Master's thesis). Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:142750

Prka, Marko. "Statistička analiza vjetra i analiza proizvodnje buduće vjetroelektrane na zadanoj lokaciji." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2019. https://urn.nsk.hr/urn:nbn:hr:200:142750

Prka, Marko. "Statistička analiza vjetra i analiza proizvodnje buduće vjetroelektrane na zadanoj lokaciji." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2019. https://urn.nsk.hr/urn:nbn:hr:200:142750

Prka, M. (2019). 'Statistička analiza vjetra i analiza proizvodnje buduće vjetroelektrane na zadanoj lokaciji', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 15 March 2023, https://urn.nsk.hr/urn:nbn:hr:200:142750

Prka M. Statistička analiza vjetra i analiza proizvodnje buduće vjetroelektrane na zadanoj lokaciji [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2019 [cited 2023 March 15] Available at: https://urn.nsk.hr/urn:nbn:hr:200:142750

M. Prka, "Statistička analiza vjetra i analiza proizvodnje buduće vjetroelektrane na zadanoj lokaciji", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:200:142750

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Metadata
TitleStatistička analiza vjetra i analiza proizvodnje buduće vjetroelektrane na zadanoj lokaciji
Title (english)Statistical analysis of wind velocity and power production analysis of the future wind park at the given location
AuthorMarko Prka
MentorPredrag Marić (mentor)
Committee memberPredrag Marić (predsjednik povjerenstva)
Committee memberGoran Knežević (član povjerenstva)
Committee memberRužica Kljajić (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Power Engineering)
(Chair of Power Systems and Substations)
Osijek
Defense date and country2019-10-31, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Power Engineering
Abstract
Statistička analiza vjetra u svrhu modeliranja proizvodnje buduće vjetroelektrane uključuje prikupljanje podataka o brzinama vjetra u što većem mogućem vremenskom intervalu. Veći vremenski period znači i veću preciznost pri simuliranju proizvodnje elektrane. Vrlo je bitno precizno modelirati proizvodnju jer o njoj ovise svi ostali faktori vjetroelektrane, od konstrukcije do prodavanja same električne energije na tržištu. Prikupili su se podatci vjetra za Split u intervalu od dvije i pola godine iz kojih su određene funkcije gustoće te kumulativne funkcije. Pri analizi vjetra obično se koriste četiri funkcije: gama, logaritamska, Rayleigh te Weibull distribucija. Iz podataka se direktno može vidjeti utjecaj topografije odabrane lokacije na distribuciju vjetra, a samim time i njenu proizvodnju. Brdoviti krajolici obično su manjeg faktora oblika čime se postiže širi opseg brzina vjetra, dok su krajolici s malo prepreka u topologiji većeg faktora oblika te konstantnijih brzina vjetra
Abstract (english)
Statistical analysis of wind for the purpose of modeling wind power generation requires gathering data of wind speeds in widest possible time period. Longer time periods corespond to greater precision when simulating the production of power plants. It is very important to model production precisely because all of the wind farm factors depend on it, from the construction of wind farm to selling the electricity on the market. The probability density function and cumulative function were derived from time series data collected in 2.5 years for Split, Croatia. Four functions are most commonly used in wind analysis: gamma, logarithmic, Rayleigh and Weibull distributions. In this study we used Weibull distribution as it is most commonly used one amongst the four. We can see the influence of the topography directly from the data derived at each site as well as it's influence on production. Mountainous landscapes usually result in smaller shape factor of Weibull distribution which means wider range of wind speeds. On the other hand, landscapes with little to no obstacles in the topography form a greater shape factor resulting in more constant wind speeds.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:142750
Study programmeTitle: Graduate University Study Programme in Electrical Engineering; specializations in: Communications and Informatics, Power Engineering
Course: Power Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka elektrotehnike (magistar/magistra inženjer/inženjerka elektrotehnike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2020-01-27 12:32:41