Economics, including all incentives, is the primary factor that drives the development of wind farms. Optimizing the wind turbine generator size-to-rotor size design based on an economic figure of merit shows that maximum wind turbine capacity factor does not yield the best economics for a given wind resource. A large rotor on a small generator will have a high capacity factor but a low annual output of electrical energy. For the same capital investment a different configuration would produce more electricity making the project more economically sound. This study varied rotor-to-generator size at a fixed capital cost and used a modified blade element momentum model to predict annual electrical energy production for each design at a given wind resource. Optimal design was the design that resulted in the highest annual electrical energy production. This was done at a series of fixed costs and a series of wind resources defined by the Weibull distribution parameters. The results indicated the following: At larger turbine sizes, (higher capital cost per turbine), the economics shifted toward a larger generator and smaller rotor (relatively). This exact relationship is dependent on the wind resource. At large turbine sizes, greater flexibility is shown in optimum generator sizing vs. rotor sizing. Having multiple generator size options for the same rotor size allows developers to more closely match and capitalize on the characteristics of their wind resource. The end result of the research is a set of diagrams developers can use to select the best turbine based on economics for their wind resource. This provides an additional tool they can use to make their projects more cost effective.
Field tests on a Skystream wind turbine in a turbulent environment were used to study the impacts of turbulence intensity on power performance. The objective of the study was to examine the influence of turbulence intensity on the deviations in power output from their published power curves, commonly experienced by small wind turbines, such that more accurate predictions in performance can be made for future small wind projects. One-minute averaged data was used to explore the distribution of turbulence intensities experienced in several wind speed bins. It was found that the resulting turbulence intensity distributions have a similar distribution across these wind speeds and thus it was compared to four common statistical distributions. A goodness of fit study was conducted finding a gamma distribution was the best fit. This finding constitutes a more elegant approach to empirically adjusting power curves for turbulence intensity than has been suggested by previous studies.
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