Statistical tools for wind energy generation
- Authors: Ndzukuma, Sibusiso
- Date: 2012
- Subjects: Wind power , Wind turbines , Winds -- Speed
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:10580 , 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.
- Full Text:
- Date Issued: 2012
- Authors: Ndzukuma, Sibusiso
- Date: 2012
- Subjects: Wind power , Wind turbines , Winds -- Speed
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:10580 , 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.
- Full Text:
- Date Issued: 2012
The development of an optimised rotor software design tool to improve performance of small horizontal axis wind turbines
- Authors: Newey, Kerryn Brett
- Date: 2012
- Subjects: Wind turbines -- Design , Wind power , Turbines -- Design
- Language: English
- Type: Thesis , Masters , MTech
- Identifier: vital:9620 , http://hdl.handle.net/10948/d1009431 , Wind turbines -- Design , Wind power , Turbines -- Design
- Description: Horizontal axis wind turbines are by far the most common and well understood forms of wind turbine. Typically a large amount of research and development has been invested in the technology of large scale wind turbines. Unfortunately, development of small machines (rotor diameter smaller than 10 metres) has not been as forthcoming. The advantages of small turbines are that they are accessible to the individual consumer and they are a very attractive project for the home builder. The disadvantage of small turbines is that due to the negative influence of economies of scale, they tend to be costly in relation to their power output and suffer from a long-term return on investment. Furthermore, trends in the wind industry have shown that smaller machines tend to be relatively simple devices that have been developed with very little research and development. As a result, small turbines can be inefficient, unreliable and expensive to maintain. In many cases rotor design is less than optimal, with very little blade refinement. This is especially critical for small rotors due to low Reynolds Number operation. Further exacerbating the problem is that the rotors are typically not well matched to the generator. In many cases the machines are not suited to the wind speed range in which they are designed to operate, reducing the financial viability due to poor performance. It is envisaged that by applying optimising techniques and automating some of the design complexities into a software design tool, more cost-effective and viable machines can be developed that will deliver improved performance and therefore become more financially viable.
- Full Text:
- Date Issued: 2012
- Authors: Newey, Kerryn Brett
- Date: 2012
- Subjects: Wind turbines -- Design , Wind power , Turbines -- Design
- Language: English
- Type: Thesis , Masters , MTech
- Identifier: vital:9620 , http://hdl.handle.net/10948/d1009431 , Wind turbines -- Design , Wind power , Turbines -- Design
- Description: Horizontal axis wind turbines are by far the most common and well understood forms of wind turbine. Typically a large amount of research and development has been invested in the technology of large scale wind turbines. Unfortunately, development of small machines (rotor diameter smaller than 10 metres) has not been as forthcoming. The advantages of small turbines are that they are accessible to the individual consumer and they are a very attractive project for the home builder. The disadvantage of small turbines is that due to the negative influence of economies of scale, they tend to be costly in relation to their power output and suffer from a long-term return on investment. Furthermore, trends in the wind industry have shown that smaller machines tend to be relatively simple devices that have been developed with very little research and development. As a result, small turbines can be inefficient, unreliable and expensive to maintain. In many cases rotor design is less than optimal, with very little blade refinement. This is especially critical for small rotors due to low Reynolds Number operation. Further exacerbating the problem is that the rotors are typically not well matched to the generator. In many cases the machines are not suited to the wind speed range in which they are designed to operate, reducing the financial viability due to poor performance. It is envisaged that by applying optimising techniques and automating some of the design complexities into a software design tool, more cost-effective and viable machines can be developed that will deliver improved performance and therefore become more financially viable.
- Full Text:
- Date Issued: 2012
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