This work describes a control system in which a cycloconverter is connected between the secondary windings of a three phase induction machine and the a. c. mains supply to give variable speed sub- and super –synchronously. In order to control the system smoothly in an asynchronous mode a secondary emf signal generator has been designed, which enables the cycloconverter to operate in synchronism with the emf induced in the secondary windings of the machine. A computer programme has been written which calculates the required firing angles for the cycloconverter to produce secondary current in phase with the secondary emf in the machine. An electronic system has been built which ensures that these firing angles are used by the cycloconverter during actual operation. A cycloconverter has been built, using an effective six phases of mains supply, and has been successfully operated over a range of 20% about synchronous speed in both generating and motoring modes. Results show the ability of the cycloconverter to drive the machine up from standstill as a motor to just below 20% subsynchronous speed. An on-line computer simulation of a wind turbine has been developed which enables an assessment of variable speed generation applied to wind turbines to be achieved. This simulation, in connection with a d. c. machine and thyristor controller, can be used to drive the shaft of the induction machine and assess operation of the cycloconverter control scheme under actual wind turbine operating conditions.

Improving performance of wind turbines through effective control strategies to reduce the power generation cost is highly desired by the wind industry. The majority of the literature on performance of wind turbines has focused on models derived from principles versed in physics. Physics-based models are usually complex and not accurate due to the fact that wind turbines involve mechanical, electrical, and software components. These components interact with each other and are subjected to variable loads introduced by the wind as well as the rotating elements of the wind turbine. Recent advances in data acquisition systems allow collection of large volumes of wind energy data. Although the prime purpose of data collection is monitoring conditions of wind turbines, the collected data offers a golden opportunity to address most challenging issues of wind turbine systems. In this dissertation, data mining is applied to construct accurate models based on the turbine collected data. To solve the data-driven models, evolutionary computation algorithms are applied. As data-driven based models are non-parametric, the evolutionary computation approach makes an ideal solution tool. Optimizing wind turbines with different objectives is studied to accomplish different research goals. Two research directions of wind turbines performance are pursued, optimizing a wind turbine performance and optimizing a wind farm performance. The goal of single wind turbine optimization is to improve wind turbine efficiency and its life-cycle. The performance optimization of a wind farm is to minimize the total cost of operating a wind farm based on the computed turbine scheduling strategies. The methodology presented in the dissertation is applicable to processes besides wind industry.

A robust model predictive control algorithm is presented that explicitly handles multiplicative, or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty in the predicted future states and inputs is bounded by polytopes. The computational cost of running the controller is reduced by calculating matrices offline that provide a means to construct outer approximations to robust constraints to be applied online. The robust algorithm is extended to problems of uncertain models with an allowed probability of violation of constraints. The probabilistic degrees of satisfaction are approximated by one-step ahead sampling, with a greedy solution to the resulting mixed integer problem. An algorithm is given to enlarge a robustly invariant terminal set to exploit the probabilistic constraints. Exponential basis functions are used to create a Robust MPC algorithm for which the predictions are defined over the infinite horizon. The control degrees of freedom are weights that define the bounds on the state and input uncertainty when multiplied by the basis functions. The controller handles multiplicative and additive uncertainty. Robust MPC is applied to the problem of wind turbine control. Rotor speed and tower oscillations are controlled by a low sample rate robust predictive controller. The prediction model has multiplicative and additive uncertainty due to the uncertainty in short-term future wind speeds and in model linearisation. Robust MPC is compared to nominal MPC by means of a high-fidelity numerical simulation of a wind turbine under the two controllers in a wide range of simulated wind conditions.

In this thesis, the effect of pitch based optimisation was explored for a 80 turbine wind farm. Using a modified Jensen wake model and the Particle Swarm Optimisation (PSO) model, a pitch optimisation strategy was created for the dominant turbulence and atmospheric condition for the wind farm. As the wake model was based on the FLORIS model developed by P.M.O Gebraad et. al., the wake and power model was compared with the FLORIS model and a -0.090% difference was found. To determine the dynamic predictive capability of the wake model, measurement values across a 10 minute period for a 19 wind turbine array were used and the wake model under predicted the power production by 17.55%. Despite its poor dynamic predictive capability, the wake model was shown to accurately match the AEP production of the wind farm when compared to a CFD simulation done in FarmFlow and only gave a 3.10% over-prediction. When the optimisation model was applied with 150 iterations and particles, the AEP production of the wind farm increased by 0.1052%, proving that the pitch optimisation method works for the examined wind farm. When the iterations and particles used for the optimisation was increased to 250, the power improvement between optimised results improved by 0.1144% at a 222.5% increase in computational time, suggesting that the solution has yet to fully converge. While the solutions did not fully converge, they converged sufficiently and an increase in iterations gave diminishing results. From the results, the pitch optimisation model was found to give a significant increase in power production, especially in wake intensive wind directions. However, the dynamic predictive capabilities will have be improved upon before the control strategy can be applied to an operational wind farm.

In this thesis, the effect of pitch based optimisation was explored for a 80 turbine wind farm. Using a modified Jensen wake model and the Particle Swarm Optimisation (PSO) model, a pitch optimisation strategy was created for the dominant turbulence and atmospheric condition for the wind farm. As the wake model was based on the FLORIS model developed by P.M.O Gebraad et. al., the wake and power model was compared with the FLORIS model and a -0.090% difference was found. To determine the dynamic predictive capability of the wake model, measurement values across a 10 minute period for a 19 wind turbine array were used and the wake model under predicted the power production by 17.55%. Despite its poor dynamic predictive capability, the wake model was shown to accurately match the AEP production of the wind farm when compared to a CFD simulation done in FarmFlow and only gave a 3.10% over-prediction. When the optimisation model was applied with 150 iterations and particles, the AEP production of the wind farm increased by 0.1052%, proving that the pitch optimisation method works for the examined wind farm. When the iterations and particles used for the optimisation was increased to 250, the power improvement between optimised results improved by 0.1144% at a 222.5% increase in computational time, suggesting that the solution has yet to fully converge. While the solutions did not fully converge, they converged sufficiently and an increase in iterations gave diminishing results. From the results, the pitch optimisation model was found to give a significant increase in power production, especially in wake intensive wind directions. However, the dynamic predictive capabilities will have be improved upon before the control strategy can be applied to an operational wind farm.

This thesis makes contributions towards the development of two different small-scale sensing systems which show promise for measurements in fluid mechanics. Well-resolved turbulent Wall Shear Stress (WSS) measurements could provide a basis for realistic computational models of near-wall turbulent flow in aerodynamic design. In aerodynamics field applications, they could provide indication of flow direction and regions of separation, enabling inputs for flight control or active control of wind-turbine blades to reduce shock and fatigue loading due to separated flow regions. Traditional thermal WSS sensors consist of a single microscale hot-film, flush-mounted with the surface and maintained at constant temperature. Their potential for fast response to small fluctuations may not be realized, as heat transfer through the substrate creates heat-exchange with fluid, leading to loss of spatial and temporal resolution. The guard-heated thermal WSS sensor is a design introduced to block this loss of resolution. A numerical flow-field with a range of length and time and scales was generated to study the response of both guard-heated and conventional single-element thermal WSS sensors. A conjugate heat transfer solution including substrate heat conduction and flow convection, provides spatiotemporal data on both the actual and the “measured” WSS fluctuations calculated from the heat transfer rates experienced due to the WSS field. For a single-element sensor in air, we found that the heat transfer through the substrate was up to six times larger than direct heat transfer from the hot-film to the fluid. The resulting loss of resolution in the single-element sensor can be largely recovered by using the guard-heated design. Spectra for calculated WSS from heat transfer response show that high frequencies are considerably better resolved in guard-heated sensors than in the single element sensor. Nanoresonators are nanowires (NWs) excited into mechanical vibration at a resonance frequency, with a change in spectral width created by gas damping from the environment, or a shift in the resonance peak frequency created by added mass. They enable a wide range of applications, from sensors to study rarefied gas flow friction to the detection of early-stage cancer. The extraordinary sensitivity of nanoresonators for disease molecule detection has been demonstrated with a few NWs, but the high cost of traditional electron-beam lithography patterning, have inhibited practical applications requiring large arrays of sensors. Field-directed assembly techniques under development in our laboratory enable a large number of devices at low cost. Electro-deposition of metals in templates yields high-quality single nanowires, but undesired clumps must be removed. This calls for separation (extraction) of single nanowires. In this work, single nanowires are extracted by using the sedimentation behavior of particles. Based on numerical and experimental analyses, the optimum time and region for extracting samples with the highest fraction of single nanowires ratio was found. We show that it is possible to take samples free of large clumps of nanowires and decrease the ratio of undesired particles to single nanowires by over one order of magnitude. / Graduate