Current wind turbine performance codes are not yet able to predict the rotor aerodynamic behaviour with sufficient certainty. This has led to both the over-design of blades and to operational restrictions in certain wind conditions. Essentially the problem is one of aerodynamic stall. Steady 3-dimensional stall can occur near the blade root in high wind conditions and may produce more power than predicted. Dynamic stall can also be expected due to the effects of yawed operation, turbulence, tower shadow and the earth's boundary layer. The main aim of this work is to provide a coherent set of measured aerodynamic data accounting for both axial/non-axial flow and stall in high winds. These measurements are designed to highlight the effects of both steady and dynamic stall on the rotor aerodynamic performance. In addition, the data will enable current performance prediction codes to be developed and validated. A completely new turbine has been designed and built at Cranfield to make aerodynamic measurements using pressure transducers. The design has been dominated by the requirements of accommodating the transducer signal processing equipment and allowing variation of many of the rotor parameters. Three commercial glass fibre blades were installed and performance curves measured on a conventional field site at a height of 11.5m for three rotor speed settings. These measurements show the turbine to give adequate power performance. A mobile trailer has been used to tow the turbine at a height of 4m along the Cranfield runways. Mobile testing facilitates an accelerated test schedule and allows aerodynamic data to be acquired under controlled wind conditions. A fully instrumented blade, fitted with forty transducers, has been tested under these circumstances and produced a large database of pressure measurements covering operation in winds up to 25 iq/s and yaw angles between -4511 and +55°. Analysis of the data has shown it to be of good quality and allowed some of the effects of yaw and stall to be identified. The use of the data base for performance prediction code validation has also been established.

Eolus recently developed, sold and now manage a wind farm consisting of four 2 MW wind turbines located in the northern half of Sweden. Soon after commissioning it was noticed that they were underperforming in terms of production and availability. It was suspected that one turbine was underperforming relative to the manufacturers’ power curve. Furthermore, the de-icing systems were discovered to be problematic, causing a lot of unplanned downtime. The main goals of this project are to determine the causes of the discrepancies in availability and production at the wind farm. As part of the investigation, the malfunctioning de-icing systems are also investigated. Initially, the background of the wind farm was researched. Important contracts, maintenance reports and other documentation was reviewed. Moreover, interviews were performed with four people involved with the wind farm. These revealed that problems first began during the construction phase. Delays and poor construction quality in general led to problems being carried over to the operations stage. Complications with the de-icing systems and blade drainage holes contributed to underperformance during the first year of operation. The second year of operation was marked by the de-icing system electrical cabinet detaching in the hub of turbine 2. Analysis of the turbine data and status files confirmed and elaborated on the information provided by the qualitative analysis. Investigation of the production and lost production figures revealed that the main problems have been related to the pitch systems, low temperature kits, sonic anemometers, PT-100 sensors, and the software for the controllers. Furthermore, a significant proportion of the lost production and downtime in years one and two can be attributed to tests and repairs performed on the de-icing systems. However, early indications in year three suggest that the single active de-icing system in turbine 3 is functioning as it should. Year three began with a significant improvement in availability, all turbines have experienced monthly availabilities of at least 90%. Overall, it appears that the fact that only one de-icing system is active has had a significant impact on the availability and production figures. However, organisational issues with the manufacturer still need to be resolved, as do the technical issues.

It is preferable that control and bearing condition monitoring are integrated, as the condition of the system should influence control actions. As wind turbines mainly work in remote areas, it becomes necessary to develop a wireless platform for the control system. A fuzzy system with self-tuning mechanism was developed. The input speed error and speed change were selected to control the shaft speed, while the kurtosis and peak-to-peak values were used as another set of inputs to monitor the bearing conditions. To enhance effectiveness, wait-and-see (WAS) logic was used as the pre-processing step for the raw vibration signal. The system was implemented on the LabVIEW platform. Experiments have shown that the system can effectively adjust motor rotating speed in response to bearing conditions. For future studies, more advanced fault detection methods can be integrated with proper tuning mechanisms to enrich the performance and function of the controller.

In the entirety of this project, a power converter is modeled, simulated, designed, and optimized to convert a three-phase AC wind turbine source to charge DC lead-acid batteries, applying new control techniques and an innqvative design to produce the most energy from the small wind turbine. The converter will implement new approaches to power factor correction and maximum power point tracking to capture the most energy under any operating conditions of the wind turbine. Overwind conditions will be protected against using the converter's ability to slow the turbine instead of usual resistive loads or mechanical braking. Other techniques to protect the batteries and the converter will be discussed in the scope of this paper. Through testing the designed converter, supporting evidence is shown whether the topology and control techniques are beneficial by comparing the degree of effectiveness of each method. The goal is to prove that these methods will provide a significant increase in energy converted.

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A neural network prediction method has been developed to compute self-noise of airfoils typically used in wind turbines. The neural networks were trained using experimental data corresponding to tests of several different airfoils over a range of flow conditions. The experimental data corresponds to the NACA 0012, Delft DU96, Sandia S831, S822 and S834, Fx63-137, SG6043 and SD-2030 airfoils. The chord of these airfoils range from 0.025 to 0.91 m and they were tested at Reynolds numbers of up to 3.8 million and angle of attack up to 15° depending on the airfoil. Using experimental data corresponding to different airfoils provides to the neural network the capacity to take into account the geometry of the airfoils in the predictions.geometry of the airfoils in the predictions. The input parameters to the network are the flow speed, chord length, effective angle of attack and parameters describing the geometrical shape of the airfoil. In addition, boundary layer displacement thickness was used for some models. The parameters used for taking into account the airfoil's geometry are based on a conformal mapping method or a polynomial approximation. The output of the neural network is given by sound pressure level in 1/3rd octave bands for nine frequencies ranging from 630 to 4000 Hz. The present work constitutes an application of neural networks to aeroacoustics. The main objective was to assess the potential of using neural networks to model airfoil noise. Therefore, this work is focused in the modeling of the problem, and no mathematical analyses about neural networks are intended. To this end, several models were investigated both in terms of the configuration and training approach. The performance of the networks was evaluated for a range of flow conditions. The neural network technique was first investigated for the NACA 0012 airfoil only. For this case, the geometry of the airfoil was not incorporated as input into the model. The neural network approach was then extended to account for airfoils of any geometry by including data from all airfoils in the training. The results show that the neural networks are capable of predicting the airfoils self-noise reasonably well for most of the flow conditions. The broadband noise due to the turbulent boundary layer interacting with the trailing edge is estimated very well. The tonal vortex shedding noise due to laminar boundary layer-trailing edge interaction is not predicted as well, most likely due to the limited data available for this noise source. In summary, the research here demonstrated the potential of the neural network as a tool to predict noise from typical wind turbine airfoils. / Master of Science

The technologies of urban wind turbines have been rapidly developed in recent years, but urban wind turbines have not found a wide application due to the limitations of their designs. The power output of urban wind turbine is significantly affected by urban terrain, which can cause low speed flow with frequent change of its direction. Thus, there is a need for a new wind turbine to meet the requirements of an urban wind turbine. In this study, a novel wind turbine for urban areas was designed and developed. The investigations of the novel urban wind turbine were carried out by using computational fluid dynamic (CFD) simulations and wind tunnel tests. The results from the investigation have shown that the novel wind turbine has a great potential to harvest wind energy in urban areas. A detailed study of effects of each parameter on wind energy concentration of the novel wind turbine was carried out with CFD simulations. According to the simulation results, the shroud structure of the novel wind turbine was modified and the dimensions of the final structure were identified. It was determined that the capability of wind energy concentration of the novel wind turbine shroud has been significantly improved through the structure optimisations. Furthermore, guide vane and impulse turbine were implemented in the novel wind turbine. The flow characteristics through the guide vane was studied and discussed. It was found that the wind flow characteristics can be properly modified by implementing guide vane and the structure of impulse turbine was suitable to be implemented in the novel wind turbine due to the flow characteristic through the guide vane.