With wind energy becoming a major source of energy, there is a pressing need to reduce all associated costs to be competitive in a market that might be fully subsidy-free in the near future. Before thousands of wind turbines were installed all over the world, research in e.g. understanding aerodynamics, developing new materials, designing better gearboxes, improving power electronics etc., helped to cut down wind turbine manufacturing costs. It might be assumed, that this would be sufficient to reduce the costs of wind energy as the resource, the wind itself, is free of costs. However, it has become clear that the operation and maintenance of wind turbines contributes significantly to the overall cost of energy. Harsh environmental conditions and the frequently remote locations of the turbines makes maintenance of wind turbines challenging. Just recently, the industry realised that a move from reactive and scheduled maintenance towards preventative or condition-based maintenance will be crucial to further reduce costs. Knowing the condition of the wind turbine is key for any optimisation of operation and maintenance. There are various possibilities to install advanced sensors and monitoring systems developed in recent years. However, these will inevitably incur new costs that need to be worthwhile and retro-fits to existing turbines might not always be feasible. In contrast, this work focuses on ways to use operational data as recorded by the turbine's Supervisory Control And Data Acquisition (SCADA) system, which is installed in all modern wind turbines for operating purposes -- without additional costs. SCADA data usually contain information about the environmental conditions (e.g. wind speed, ambient temperature), the operation of the turbine (power production, rotational speed, pitch angle) and potentially the system's health status (temperatures, vibration). These measurements are commonly recorded in ten-minutely averages and might be seen as indirect and top-level information about the turbine's condition. Firstly, this thesis discusses the use of operational data to monitor the power performance to assess the overall efficiency of wind turbines and to analyse and optimise maintenance. In a sensitivity study, the financial consequences of imperfect maintenance are evaluated based on case study data and compared with environmental effects such as blade icing. It is shown how decision-making of wind farm operators could be supported with detailed `what-if' scenario analyses. Secondly, model-based monitoring of SCADA temperatures is investigated. This approach tries to identify hidden changes in the load-dependent fluctuations of drivetrain temperatures that can potentially reveal increased degradation and possible imminent failure. A detailed comparison of machine learning regression techniques and model configurations is conducted based on data from four wind farms with varying properties. The results indicate that the detailed setup of the model is very important while the selection of the modelling technique might be less relevant than expected. Ways to establish reliable failure detection are discussed and a condition index is developed based on an ensemble of different models and anomaly measures. However, the findings also highlight that better documentation of maintenance is required to further improve data-driven condition monitoring approaches. In the next part, the capabilities of operational data are explored in a study with data from both the SCADA system and a Condition Monitoring System (CMS) based on drivetrain vibrations. Analyses of signal similarity and data clusters reveal signal relationships and potential for synergistic effects of the different data sources. An application of machine learning techniques demonstrates that the alarms of the commercial CMS can be predicted in certain cases with SCADA data alone. Finally, the benefits of having wind turbines in farms are investigated in the context of condition monitoring. Several approaches are developed to improve failure detection based on operational statistics, CMS vibrations or SCADA temperatures. It is demonstrated that utilising comparisons with neighbouring turbines might be beneficial to get earlier and more reliable warnings of imminent failures. This work has been part of the Advanced Wind Energy Systems Operation and Maintenance Expertise (AWESOME) project, a European consortium with companies, universities and research centres in the wind energy sector from Spain, Italy, Germany, Denmark, Norway and UK. Parts of this work were developed in collaboration with other fellows in the project (as marked and explained in footnotes).

Small wind turbine installations have a variety of potential uses, each with unique performance demands and operating conditions. Many applications require that the turbine is placed in wind conditions that are not ideal for optimum operation. Better predictive techniques can improve wind turbine performance through improved control strategies and enhanced designs. Conventional methods of wind power design and control utilize an average power coefficient. In this thesis, various techniques to predict the transient power coefficient of a wind turbine are developed. The operation of a Savonius wind turbine is accurately represented, with a new model which considers the flow distributions to predict the changes in power output at all rotor positions. Another model is developed that represents the dynamics of a small horizontal wind turbine, including the effect of transient wind conditions on rotor speed and acceleration. These can supplement current methods to determine turbine placement, selection and categorization.

Currently, wind energy presents an excellent opportunity to satisfy the growing demand without the supply and environmental problems associated with conventional energy. The engineering in wind turbines is not fully mature. There are still phenomenon, particularly dynamic stall, that cannot accurately be modeled or controlled. Dynamic stall contributes to fatigue stress and premature failure in many turbine components. The three dimensionality of dynamic stall makes these structures unique for wind turbines. Currently, flow visualization of dynamic stall on a wind turbine rotor has not been achieved. These visualizations can reveal a lot about the structures that contribute to dynamic stall. Particle Image Velocimetry (PIV) is a powerful experimental technique that can take multiple non-intrusive flow measurements simultaneously of planar flow. Using high-speed cameras time resolved PIV can reveal the transient development of a given flow field. This technique is ideally suited to gain a better understanding of dynamic stall. A custom wind turbine is being built at the University of Waterloo to allow such measurements on the blade. A high speed camera is mounted on the hub and will take measurements within the rotating domain. Mirrors are used so that laser illumination rotates with the blade. The wind turbine will operate in controlled conditions provided by a large wind tunnel. High speed pressure data acquisition will be used in conjunction with PIV to get an understanding of the forces associated with the flow structures. Computational fluid dynamics was used to size the rotor within the wind tunnel. Laser based measurements required special considerations for stiffness. Many revealing experiments will be made possible by this apparatus. First, the flow structures responsible for the various forces can be identified. Quantitative measurements of the flow field will identify the development of the stall vortex. The quantified flow structures can be used verify and improve models. The high spatial resolution of PIV can map the three dimensional flow structure in great detail. The experimental apparatus is independent of the blade geometry, as such multiple blades can be used to identify the effect of blade geometry. Finally flow control research in the field of aviation can be applied to control dynamic stall.

In this thesis it has been investigated if LQG control could be used to mitigate torsional oscillations in a variable speed, fixed pitch wind turbine. The wind turbine is a vertical axis wind turbine with a 40 m tall axis that is connected to a generator. The power extracted by the turbine is delivered to the grid via a passive rectifier and an inverter. By controlling the grid side inverter the current is controlled and hence the rotational speed can be controlled. A state space model was developed for the LQG controller. The model includes both the dynamics of the electrical system as swell as the two mass system, consisting of the turbine and the generator connected with a flexible shaft. The controller was designed to minimize a quadratic criterion that punishes both torsional oscillations, command following and input signal magnitude. Integral action was added to the controller to handle the nonlinear aerodynamic torque. The controller was compared to the existing control system that uses a PI controller to control the speed, and tested usingMATLAB Simulink. Simulations show that the LQG controller is just as good as the PI controller in controlling the speed of the turbine, and has the advantage that it can be tuned such that the occurrence of torsional oscillations is mitigated. The study also concluded that some external method of dampening torsional oscillations should be implemented to mitigate torsional oscillations in case of a grid fault or loss of PWM signal.

Currently, wind energy presents an excellent opportunity to satisfy the growing demand without the supply and environmental problems associated with conventional energy. The engineering in wind turbines is not fully mature. There are still phenomenon, particularly dynamic stall, that cannot accurately be modeled or controlled. Dynamic stall contributes to fatigue stress and premature failure in many turbine components. The three dimensionality of dynamic stall makes these structures unique for wind turbines. Currently, flow visualization of dynamic stall on a wind turbine rotor has not been achieved. These visualizations can reveal a lot about the structures that contribute to dynamic stall. Particle Image Velocimetry (PIV) is a powerful experimental technique that can take multiple non-intrusive flow measurements simultaneously of planar flow. Using high-speed cameras time resolved PIV can reveal the transient development of a given flow field. This technique is ideally suited to gain a better understanding of dynamic stall. A custom wind turbine is being built at the University of Waterloo to allow such measurements on the blade. A high speed camera is mounted on the hub and will take measurements within the rotating domain. Mirrors are used so that laser illumination rotates with the blade. The wind turbine will operate in controlled conditions provided by a large wind tunnel. High speed pressure data acquisition will be used in conjunction with PIV to get an understanding of the forces associated with the flow structures. Computational fluid dynamics was used to size the rotor within the wind tunnel. Laser based measurements required special considerations for stiffness. Many revealing experiments will be made possible by this apparatus. First, the flow structures responsible for the various forces can be identified. Quantitative measurements of the flow field will identify the development of the stall vortex. The quantified flow structures can be used verify and improve models. The high spatial resolution of PIV can map the three dimensional flow structure in great detail. The experimental apparatus is independent of the blade geometry, as such multiple blades can be used to identify the effect of blade geometry. Finally flow control research in the field of aviation can be applied to control dynamic stall.

Abstract Energy is one of the most important factors that continue to influence the shape of civilization in the 21st Century. The cost and availability of energy significantly impacts our quality of life, the health of national economies and the stability of our environment. In recent years there has been a significant global commitment to develop clean and alternative forms of energy resources and it is envisioned that by 2020 10% of world energy will be supplied from renewable resources, and there is an expectation that this value will grow to 50% by 2050. Among renewable energy resources, wind generation technology has matured considerably, and wind is fairly distributed around the globe and therefore available to world communities. In the last decade, wind generation has been the fastest growing energy source globally. However more penetration of wind energy into existing power networks raises concern for power system operators and regulators. Traditionally wind energy convertors do not participate in frequency regulation or Automatic Generation Control (AGC) services, and therefore large penetration of wind power into the power systems can result in a reduction of total system inertia and robustness of the frequency response to the disturbances. The research presented in this thesis covers some of the operational and design aspects of frequency control and AGC services in power systems with mixed generation resources. The thesis examines the operation of the Doubly Fed Induction Generator (DFIG) with a modified inertial loop control considering single-area and two-area frequency control, both primary control and AGC. The thesis presents new, small-perturbation, linear, dynamic, mathematical models for the simulation of primary regulation services and AGC services for single-area and two-area power systems with a mix of conventional and non-conventional DFIG-based wind generators. In order to improve the performance of the frequency regulation and AGC services of the above systems, a parameter optimization technique based on the minimization of the Integral of Squared Errors (ISE) is applied to determine the optimal settings for the proportional-integral (PI) controller gains of the DFIG machines. The thesis presents analytical studies with various perturbations to demonstrate the effectiveness and participation of DFIG-based wind generators in frequency support services and draws some important conclusions. Variation in DFIG penetration levels, and wind speed levels (strong wind and weak wind) on system frequency control performance, has also been examined in the thesis.

¡@¡@The dynamic power flow in a dual-input parallel planetary gear train system is simulated in this study. Different wind powers for the small wind turbines are merged to the synchronous generator in this system to simplify and reduce the cost of the system. Nonlinear equations of motion of these gears in the planetary system are derived. The fourth order Runge-Kutta method has employed to calculate the time varied torque, root stress and Hertz stress between engaged gears. The genetic optimization method has also applied to derive the optimized tooth form factors, e.g. module and the tooth face width. ¡@¡@The dynamic power flow patterns in this dual input system under various input conditions, e.g. two equal and unequal input powers, only single available input power, have been simulated and illustrated. The corresponding dynamic stress and safety factor variations have also been explored. Numerical results reveal that the proposed dual-input planetary gear system is feasible. To improve the efficiency of this wind power generation system. An inertia variable flywheel system has also been added at the output end to store or release the kinetic energies at higher or lower wind speed cases. A magnetic density variable synchronous generator has also been studied in this work to investigate the possible efficiency improvement in the system. Numerical results indicate that these inertia variable flywheel and magnetic density variable generator may have advantages in power generation.

This research study developed basic dynamic models that can be used to accurately predict the response behavior of a near-shore wind turbine structure with monopile, suction caisson, or gravity-based foundation systems. The marine soil conditions were modeled using apparent fixity level, Randolph elastic continuum, and modified cone models. The offshore wind turbine structures were developed using a finite element formulation. A two-bladed 3.0 megawatt (MW) and a three-bladed 1.5 MW capacity wind turbine were studied using a variety of design load, and soil conditions scenarios. Aerodynamic thrust loads were estimated using the FAST Software developed by the U.S Department of Energy’s National Renewable Energy Laboratory (NREL). Hydrodynamic loads were estimated using Morison’s equation and the more recent Faltinsen Newman Vinje (FNV) theory. This research study addressed two of the important design constraints, specifically, the angle of the support structure at seafloor and the horizontal displacement at the hub elevation during dynamic loading. The simulation results show that the modified cone model is stiffer than the apparent fixity level and Randolph elastic continuum models. The effect of the blade pitch failure on the offshore wind turbine structure decreases with increasing water depth, but increases with increasing hub height of the offshore wind turbine structure.

Wind turbines operate in a constantly changing wind environment. This requires modeling and simulation of extreme events in which the wind turbine operates and a study of associated turbine loads as part of the design practice and/or site assessment. Thunderstorms are transient atmospheric events that occur frequently in some regions of the world and can influence the design of a wind turbine. Downbursts are extreme surface winds that are produced during a thunderstorm. They are both complex to model and their damaging effect on wind turbines has been noted in recent years. In the last few decades, downbursts have been the subject of studies in various fields--- most notably, in aviation. Despite their complexity, generally only empirical models based on observational data have been developed for practical uses. Based on such field data as well as laboratory tests, it is common to model a downburst as a jet impingement on a flat plate. The actual buoyancy-driven flow has been commonly modeled as an equivalent momentum flux-driven flow resulting from the impinging jet. The use of computational fluid dynamics (CFD) to model a downburst based on the idea of an impinging jet offers an alternative approach to experimental and analytical approaches. Simulation of "downburst"' wind fields using a computational model and analysis of associated loads on a wind turbine operating during such events is the subject of this study. Although downburst-like events have been simulated using commercial CFD software, the resulting wind fields from such simulations have not been used as inflow fields for wind turbine loads analysis. In this study, the commercial CFD software, ANSYS FLUENT 12.0, is used to simulate downburst events and the output wind fields are used as input to loads analysis for a utility-scale 5-MW wind turbine. The inflow wind fields are represented by both non-turbulent and turbulent components---the former are simulated using FLUENT while the latter are simulated as stochastic processes using Fourier techniques together with standard turbulence power spectral density functions and coherence functions. The CFD-based non-turbulent wind fields are compared with those from empirical/analytical approaches; turbine loads are also compared for the two approaches. The study suggests that a CFD-based approach can capture similar wind field characteristics as are modeled in the alternative approach; associated turbine loads are as well not noticeably different with the two approaches. / text

The International Electrotechnical Commission (IEC) standard 61400-1 for the design of wind turbines does not explicitly address site-specific conditions associated with anomalous atmospheric events or conditions. Examples of such off-standard atmospheric conditions include thunderstorm downbursts, hurricanes, tornadoes, low-level jets, etc. This study is focused on the simulation of thunderstorm downbursts using a deterministic-stochastic hybrid model and the prediction of wind turbine loads resulting from these simulated downburst wind fields. The wind velocity field model for thunderstorm downburst simulation is first discussed; in this model, downburst winds are generated separately from non-turbulent and turbulent parts. The non-turbulent part is based on an available analytical model (with some modifications), while the turbulent part is simulated as a stochastic process using standard turbulence power spectral density functions and coherence functions. Tower and rotor loads are generated using simulation of the aeroelastic response for models of utility-scale wind turbines. The main objective is to improve our understanding from the point of view of design so that we may begin to address transient events such as thunderstorm downbursts based on the simulations carried out in this research study. The study discusses as well the role of control systems (for blade pitch and turbine yaw), of models for representing transient turbulence characteristics, and of correlated demand and loads on multiple units in turbine arrays during thunderstorm downbursts. / text