Evaluating the suitability of wind speed probability distribution models: A case of study of east and southeast parts of Iran

2016 ◽  
Vol 119 ◽  
pp. 101-108 ◽  
Author(s):  
Omid Alavi ◽  
Kasra Mohammadi ◽  
Ali Mostafaeipour
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Distribution Models

Increase Power Efficientand Reliability of Electrosupply of Consumers of Low Power

GIS Business ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. 42-52
Author(s):  
Sadullayev Nasillo Nematovich ◽  
Safarov Alisher Bekmurodovich ◽  
Nematov Shuhrat Nasilloyevich ◽  
Mamedov Rasul Akif- Ogli
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Low Power ◽  
Electric Power ◽  
Energy Sources ◽  
Wind Flow ◽  
Wind Rose ◽  
Energy Potential ◽  
Wind Power Installation ◽  
Wind Energy Potential

This article assesses the wind speed data and wind energy potential in the Bukhara region of Uzbekistan. In article it is stated a principle construction "hybrid" a source of the electric power consisting from wind power installation with mechanical store of energy, the solar panel with аккумулятор in common working with an electric network. The speed and direction of the wind measured at a height of 10 m were analyzed by the Weibull probability distribution functionTo determine the direction of wind flow (wind rose), a graph in Matlab environment was constructed. The method of an estimation energy of efficiency of the objects eating from several energy sources is offered. It is proved efficiency of application of such source of the electric power low power consumers

The Research of Wind Power Fluctuation in Different Time Intervals

2014 ◽  
Vol 953-954 ◽  
pp. 458-461
Author(s):  
Yi Hui Zhang
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Wind Power ◽  
Wind Farm ◽  
Time Window ◽  
Missing Information ◽  
Power Fluctuation ◽  
Time Intervals ◽  
Trend Pattern ◽  
Length Of Interval

Power from wind turbines is mainly related to the wind speed. Due to the influence of the uncertainty of the wind, intermittent and wind farm in units of the wake, wind power has fluctuations. Based on the field measurement, it is found that t location-scale distribution is suitable to identify the probability distribution of wind power variations. By analyzing the fluctuation of a single in different time intervals, we find that the distribution of wind power fluctuation possesses a certain trend pattern. With the length of the time window increasing, the fluctuations increase and some information has been missed. We define an index to calculate the quantity of missing information and can use that to evaluate whether a certain length of interval is acceptable.

scholarly journals Mixture probability distribution functions using novel metaheuristic method in wind speed modeling

2021 ◽  
Author(s):  
Amr Khaled Khamees ◽  
Almoataz Y. Abdelaziz ◽  
Ziad M. Ali ◽  
Mosleh M. Alharthi ◽  
Sherif S.M. Ghoneim ◽  
...  
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Distribution Functions ◽  
Probability Distribution Functions

Risk Planning with Discrete Distribution Analysis Applied to Petroleum Spills

2013 ◽  
Vol 2 (4) ◽  
pp. 61-78 ◽  
Author(s):  
Roy L. Nersesian ◽  
Kenneth David Strang
Keyword(s):  
Probability Distribution ◽  
Probability Distributions ◽  
Low Cost ◽  
Discrete Distribution ◽  
Distribution Model ◽  
Distribution Analysis ◽  
Distribution Models ◽  
Discrete Probability ◽  
Nonparametric Statistical ◽  
Spreadsheet Software

This study discussed the theoretical literature related to developing and probability distributions for estimating uncertainty. A theoretically selected ten-year empirical sample was collected and evaluated for the Albany NY area (N=942). A discrete probability distribution model was developed and applied for part of the sample, to illustrate the likelihood of petroleum spills by industry and day of week. The benefit of this paper for the community of practice was to demonstrate how to select, develop, test and apply a probability distribution to analyze the patterns in disaster events, using inferential parametric and nonparametric statistical techniques. The method, not the model, was intended to be generalized to other researchers and populations. An interesting side benefit from this study was that it revealed significant findings about where and when most of the human-attributed petroleum leaks had occurred in the Albany NY area over the last ten years (ending in 2013). The researchers demonstrated how to develop and apply distribution models in low cost spreadsheet software (Excel).

Determining suitable region wind speed probability distribution using optimal score-radar map

2019 ◽  
Vol 183 ◽  
pp. 590-603 ◽  
Author(s):  
Shuwei Miao ◽  
Yingzhong Gu ◽  
Dan Li ◽  
Han Li
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Optimal Score ◽  
Radar Map

scholarly journals Frequency Distribution Model of Wind Speed Based on the Exponential Polynomial for Wind Farms

2019 ◽  
Vol 11 (3) ◽  
pp. 665 ◽  
Author(s):  
Lingzhi Wang ◽  
Jun Liu ◽  
Fucai Qian
Keyword(s):  
Wind Speed ◽  
Frequency Distribution ◽  
Least Squares Method ◽  
Wind Farms ◽  
Rayleigh Distribution ◽  
Distribution Model ◽  
Exponential Polynomial ◽  
Distribution Models ◽  
Wind Speed Data ◽  
Wind Speeds

This study introduces and analyses existing models of wind speed frequency distribution in wind farms, such as the Weibull distribution model, the Rayleigh distribution model, and the lognormal distribution model. Inspired by the shortcomings of these models, we propose a distribution model based on an exponential polynomial, which can describe the actual wind speed frequency distribution. The fitting error of other common distribution models is too large at zero or low wind speeds. The proposed model can solve this problem. The exponential polynomial distribution model can fit multimodal distribution wind speed data as well as unimodal distribution wind speed data. We used the linear-least-squares method to acquire the parameters for the distribution model. Finally, we carried out contrast simulation experiments to validate the effectiveness and advantages of the proposed distribution model.

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