O
Omid Alavi
Researcher at K.N.Toosi University of Technology
Publications - 30
Citations - 743
Omid Alavi is an academic researcher from K.N.Toosi University of Technology. The author has contributed to research in topics: Wind power & Wind speed. The author has an hindex of 9, co-authored 28 publications receiving 564 citations. Previous affiliations of Omid Alavi include University of Hasselt.
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Assessing different parameters estimation methods of Weibull distribution to compute wind power density
TL;DR: In this paper, the effectiveness of six numerical methods is evaluated to determine the shape (k) and scale (c) parameters of Weibull distribution function for the purpose of calculating the wind power density.
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Evaluating the suitability of wind speed probability distribution models: A case of study of east and southeast parts of Iran
TL;DR: In this article, the suitability of different probability functions for estimating wind speed distribution at five stations, distributed in the east and southeast of Iran, was evaluated for the first time to estimate the distribution of wind speed.
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Use of Birnbaum-Saunders distribution for estimating wind speed and wind power probability distributions: A review
TL;DR: In this paper, application of the two parameter Birnbaum-Saunders (BS) distribution is introduced and reviewed for characterizing the wind speed and wind power density distributions, and the suitability of the BS distribution was evaluated against nine earlier used one-component distributions.
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Analysis of hydrogen production from wind energy in the southeast of Iran
TL;DR: In this paper, the capability of wind energy for producing hydrogen in the south eastern province of Sistan & Baluchestan in Iran was investigated, and four different wind turbines with a capacity of 300-900kW in five locations, in Dalgan, Lutak, Mil-Nader, Nosratabad and Zahedan, were analyzed.
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Sensitivity analysis of different wind speed distribution models with actual and truncated wind data: A case study for Kerman, Iran
TL;DR: In this paper, the best fit functions to actual and truncated wind speed data are selected by examining nine goodness-of-fit statistics, and it is observed that the lognormal function gives a better fit to the actual data, while the Weibull model performs better using the truncated data.