Abstract:
A statistical technique to characterize wind speed data for use in wind energy conversion system (WECS) is presented. We start by examining the frequency distribution of long-term data. The histogram is generated for observed wind speed for a particular hour over a month for a number of years. It is fitted to two distributions (Weibull, and β).Genetic Programming (GP) also used to fit the distribution. Two goodness-of-fit criteria are employed in checking the best fit. These are Chi-square (χ2), and Kolmogorov-Smirnov (KS). Long-term data from International Logan Airport, Boston, MA, USA are analyzed. It is found that the GP fits the long-term hourly data best.
Gouda, S. (2006). Using Genetic Programming to Estimate WECS Output Power. The International Conference on Mathematics and Engineering Physics, 3(International Conference on Engineering Mathematics and Physics (ICMEP-3)), 1-8. doi: 10.21608/icmep.2006.29911
MLA
Said A. Gouda. "Using Genetic Programming to Estimate WECS Output Power". The International Conference on Mathematics and Engineering Physics, 3, International Conference on Engineering Mathematics and Physics (ICMEP-3), 2006, 1-8. doi: 10.21608/icmep.2006.29911
HARVARD
Gouda, S. (2006). 'Using Genetic Programming to Estimate WECS Output Power', The International Conference on Mathematics and Engineering Physics, 3(International Conference on Engineering Mathematics and Physics (ICMEP-3)), pp. 1-8. doi: 10.21608/icmep.2006.29911
VANCOUVER
Gouda, S. Using Genetic Programming to Estimate WECS Output Power. The International Conference on Mathematics and Engineering Physics, 2006; 3(International Conference on Engineering Mathematics and Physics (ICMEP-3)): 1-8. doi: 10.21608/icmep.2006.29911
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