Estimation of wind direction distribution with genetic algorithms
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F13%3A39896608" target="_blank" >RIV/00216275:25530/13:39896608 - isvavai.cz</a>
Výsledek na webu
<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6567681&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6567660%29" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6567681&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6567660%29</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CCECE.2013.6567681" target="_blank" >10.1109/CCECE.2013.6567681</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Estimation of wind direction distribution with genetic algorithms
Popis výsledku v původním jazyce
Directional and stream data are common in many research fields. Wind speed and direction are the most important variables for effective wind energy utilization. It is also well known, that wind significantly influences the current-carrying capacity of overhead power transmission lines. This shows the importance of knowing the annual wind direction distribution for specific locations, e. g. where wind farms or power transmission lines are situated. In this paper, a new method of wind direction distribution determination is presented. The statistical model is composed of a finite mixture of circular von Mises distributions. Parameters of the model are estimated using the heuristic search method of genetic algorithms. The quality of computed distributionis evaluated by Pearson's chi-squared test. The entire proposed procedure is tested using a case study. The results show that the model composed of a finite mixture of von Mises distribution corresponds to the input data with high signifi
Název v anglickém jazyce
Estimation of wind direction distribution with genetic algorithms
Popis výsledku anglicky
Directional and stream data are common in many research fields. Wind speed and direction are the most important variables for effective wind energy utilization. It is also well known, that wind significantly influences the current-carrying capacity of overhead power transmission lines. This shows the importance of knowing the annual wind direction distribution for specific locations, e. g. where wind farms or power transmission lines are situated. In this paper, a new method of wind direction distribution determination is presented. The statistical model is composed of a finite mixture of circular von Mises distributions. Parameters of the model are estimated using the heuristic search method of genetic algorithms. The quality of computed distributionis evaluated by Pearson's chi-squared test. The entire proposed procedure is tested using a case study. The results show that the model composed of a finite mixture of von Mises distribution corresponds to the input data with high signifi
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
BB - Aplikovaná statistika, operační výzkum
OECD FORD obor
—
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2013
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
2013 26th Annual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)
ISBN
978-1-4799-0033-6
ISSN
—
e-ISSN
—
Počet stran výsledku
4
Strana od-do
78-81
Název nakladatele
IEEE (Institute of Electrical and Electronics Engineers)
Místo vydání
Piscataway
Místo konání akce
Regina
Datum konání akce
5. 5. 2013
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
000326863300014