Abstract
Modeling wind generation for use in many power system applications requires a massive database of historical wind speeds so that the stochastic nature of the wind at a particular site can be accurately analyzed. The alternative is to use reliable estimates of a probability distribution function (PDF) that can preserve the variable characteristics of wind speed and generate the desired synthetic data. This paper presents a statistical evaluation study for different collections of PDFs in order to find the best model to precisely reflect the variable characteristics of the wind at a particular site. The most commonly used PDFs, along with some advanced PDFs, have been verified against the observed wind data based on consideration of two well-known goodness of fit statistical tests. A further case study is conducted in order to evaluate the impact of sample size on the selection of the best-fit PDFs. From a variety of candidate PDFs, the results indicate that the Generalized Logistic and Dagum distributions are the PDFs that best maintain the main characteristics of the observed wind data.
| Original language | English |
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| Title of host publication | 2016 IEEE Electrical Power and Energy Conference, EPEC 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509019199 |
| DOIs | |
| State | Published - 5 Dec 2016 |
| Externally published | Yes |
Publication series
| Name | 2016 IEEE Electrical Power and Energy Conference, EPEC 2016 |
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Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- Goodness of fit tests
- probability distribution function
- wind speed data
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment
- Control and Optimization
