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Thesis (doctoral)--Lappeenranta University of Technology, 2000? / Includes bibliographical references. Also available on the World Wide Web.

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In attempting to control the power output of a wind farm, it is first necessary to smooth the power fluctuations due to wind turbulence. This is accomplished by spatial smoothing, whereby the high frequency power components of a single wind turbine generator (WTG) is reduced by a factor of N-1/2, where N is the number of WTGs in the farm. For this reason the first part of the thesis is concerned with developing a model of smoothing in a wind farm and justifying it mathematically. / After spatial smoothing, the wind farm output still contains low frequency fluctuations. The second part of the thesis makes use of a combination of: (i) pitch angle control of the turbine blades, (ii) power electronic control of the generators, (iii) spatial filtering and (iv) negative feedback control to remove the low frequency fluctuations. The wind farm output then has the quality to be sold as regulated power which fetches a better economic return than when sold as energy. This, of course, presumes that 1-hour ahead prediction of wind velocity at 1-hour long low variance is available. / The thesis also considers the case when the conditions for regulated power are not predicted. In this situation, the wind farm may opt to use the tracking mode which tracks the slowly time varying non-turbulent wind power. The thesis examines the possibility of diverting some of the wind farm power to implement dynamic performance enhancement strategies, for system damping for example. / The controllability of the wind farm is demonstrated by simulations of a wind farm made up of 24 wind turbine-generators (WTGs) using 1-hour long wind velocity data.

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The world market-place for steam turbine products is becoming increasingly competitive, and manufacturers must routinely produce designs which are extensively optimised whilst working within demanding tender and contract lead-times. The objective of the research work has been to develop a methodology whereby established turbomachinery analysis methods can be integrated within a framework of optimising algorithms. A rule-base, numerical optimisation, fuzzy logic, and genetic algorithms are used to optimise bladepath configurations, with particular emphasis on the minimisation of life-cycle operating costs. Significantly, automation of the design process is increased, design lead-times can be reduced, and performance improvements are predicted. The optimisation procedure relies on a sequential approach, with much emphasis placed on the iterative running of simple design codes. Simplified design methods are often reliant on correlated loss data to predict turbine performance, and in some cases this data is inaccurate or incomplete. An example of this is in the design of partially-admitted control stages, where little published data is available. It is suggested that CFD methods can, in some cases, be applied to derive new performance correlations or re-assess the validity of existing models. The application of an unsteady CFD solver to typical control stage geometries is presented in detail, and the approach is extended to include the development of a new control stage optimisation method.