- Title
- Statistical tools for wind energy generation
- Creator
- Ndzukuma, Sibusiso
- Subject
- Wind power
- Subject
- Wind turbines
- Subject
- Winds -- Speed
- Date Issued
- 2012
- Date
- 2012
- Type
- Thesis
- Type
- Masters
- Type
- MSc
- Identifier
- vital:10580
- Identifier
- http://hdl.handle.net/10948/d1020627
- Description
- In this study we conduct wind resource assessment to evaluate the annual energy production of a wind turbine. To estimate energy production of a wind turbine over a period of time, the power characteristics of the wind turbine are integrated with the probabilities of the wind speed expected at a chosen site. The first data set was obtained from a wind farm in Denmark. We propose several probability density functions to model the distribution of the wind speed. We use techniques from nonlinear regression analysis to model the power curve of a wind turbine. The best fit distribution model is assessed by performing numeric goodness–of–fit measures and graphical analyses. Johnson’s bounded (SB) distribution provides the best fit model with the smallest Kolmogorov–Smirnov (K-S) test statistic . 15. The four parameter logistic nonlinear regression (4PL) model is determined to provide the best fit to the power curve data, according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The estimated annual energy yield is compared to the actual production of the wind turbine. Our models underestimate the actual energy production by a 1 difference. In Chapter Six we conduct data processing, analyses and comparison of wind speed distributions using a data set obtained from a measuring wind mast mounted in Humansdorp, Eastern Cape. The expected annual energy production is estimated by using the certified power curve as provided by the manufacturer of the wind turbine under study. The commonly used Weibull distribution is determined to provide the best fit distribution model to our selected models. The annual energy yield is estimated at 7.33 GWh, with a capacity factor of 41.8 percent.
- Format
- 86 leaves
- Format
- Publisher
- Nelson Mandela Metropolitan University
- Publisher
- Faculty of Science
- Language
- English
- Rights
- Nelson Mandela Metropolitan University
- Hits: 840
- Visitors: 1002
- Downloads: 227
Collections
NMMU School of Computer Science, Mathematics, Physics and Statistics
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCEPDF | 2 MB | Adobe Acrobat PDF | View Details Download |