Wind tunnel studies of the wake behind model wind turbines with one, two and three blades have been made in order to get a better understanding of wake development as well as the possibility to predict the power output from downstream turbines working in the wake of an upstream one. Both two-component hot-wire anemometry and particle image velocimetry (PIV) have been used to map the flow field downstream as well as upstream the turbine. All three velocity components were measured both for the turbine rotor normal to the oncoming flow as well as with the turbine inclined to the free stream direction (the yaw angle was varied from 0 to 30 degrees). The measurements showed, as expected, a wake rotation in the opposite direction to that of the turbine. A yawed turbine is found to clearly deflect the wake flow to the side showing the potential of controlling the wake position by yawing the turbine. The power output of a yawed turbine was found to depend strongly on the rotor. The possibility to use active wake control by yawing an upstream turbine was evaluated and was shown to have a potential to increase the power output significantly for certain configurations. An unexpected feature of the flow was that spectra from the time signals showed the appearance of a low frequency fluctuation both in the wake and in the flow outside. This fluctuation was found both with and without free stream turbulence and also with a yawed turbine. The non-dimensional frequency (Strouhal number) was independent of the freestream velocity and turbulence level but increases with the yaw angle. However the low frequency fluctuations were only observed when the tip speed ratio was high. Porous discs have been used to compare the meandering frequencies and the cause in wind turbines seems to be related to the blade rotational frequency. It is hypothesized that the observed meandering of wakes in field measurements is due to this shedding. / QC 20101018

<p>Wind tunnel studies of the wake behind a model wind turbine have been made in order to get a better understanding of wake development as well as the possibility to predict the power output from downstream turbines working in the wake of an upstream one. Both two-component hot-wire anemometry as well as particle image velocimetry (PIV) have been used to map the flow field. All three velocity components were measured both for the turbine rotor normal to the oncoming flow as well as with the turbine inclined to the free stream direction (the yaw angle was varied from 0 to 30 degrees). The measurements showed, as expected, a wake rotation in the opposite direction to that of the turbine. A yawed turbine is found to clearly deflect the wake flow to the side showing the potential of controlling the wake position by yawing the turbine. The power output of a yawed turbine was found to vary nearly as the square of the cosine of the yaw angle. The possibility to use active wake control by yawing an upstream turbine was evaluated and was shown to have a potential to increase the power output significantly for certain configurations. An unexpected feature of the flow was that spectra from the time signals showed the appearance of a low frequency fluctuation both in the wake and in the flow outside. This fluctuation was found both with and without free stream turbulence and also with a yawed turbine. The non-dimensional frequency (Strouhal number) was independent of the free-stream velocity and turbulence level but increases with the yaw angle. However the low frequency fluctuations were only observed when the tip speed ratio (or equivalently the drag coefficient) was high. This is in agreement with the idea that the turbine shed structures as a bluff body. It is hypothesized that the observed meandering of wakes in field measurements is due to this shedding.</p>

<p>Wind tunnel studies of the wake behind model wind turbines with one, two and three blades have been made in order to get a better understanding of wake development as well as the possibility to predict the power output from downstream turbines working in the wake of an upstream one. Both two-component hot-wire anemometry and particle image velocimetry (PIV) have been used to map the flow field downstream as well as upstream the turbine. All three velocity components were measured both for the turbine rotor normal to the oncoming flow as well as with the turbine inclined to the free stream direction (the yaw angle was varied from 0 to 30 degrees). The measurements showed, as expected, a wake rotation in the opposite direction to that of the turbine. A yawed turbine is found to clearly deflect the wake flow to the side showing the potential of controlling the wake position by yawing the turbine. The power output of a yawed turbine was found to depend strongly on the rotor. The possibility to use active wake control by yawing an upstream turbine was evaluated and was shown to have a potential to increase the power output significantly for certain configurations. An unexpected feature of the flow was that spectra from the time signals showed the appearance of a low frequency fluctuation both in the wake and in the flow outside. This fluctuation was found both with and without free stream turbulence and also with a yawed turbine. The non-dimensional frequency (Strouhal number) was independent of the freestream velocity and turbulence level but increases with the yaw angle. However the low frequency fluctuations were only observed when the tip speed ratio was high. Porous discs have been used to compare the meandering frequencies and the cause in wind turbines seems to be related to the blade rotational frequency. It is hypothesized that the observed meandering of wakes in field measurements is due to this shedding.</p>

To mitigate high voltages in transmission systems with low demands, traditional solutions often consider the installation of reactive power compensators. The deployment and tuning of numbers of VAr compensators at various locations may not be cost-effective. This thesis presents an alternative method that utilises existing parallel transformers in distribution networks to provide reactive power supports for transmission systems under low demands. The operation of parallel transformers in small different tap positions, i.e. with staggered taps, can provide a means of absorbing reactive power. The aggregated reactive power absorption from many pairs of parallel transformers could be sufficient to provide voltage support to the upstream transmission network. Network capability studies have been carried out to investigate the reactive power absorption capability through the use of tap stagger. The studies are based on a real UK High Voltage distribution network, and the tap staggering technique has been applied to primary substation transformers. The results confirm that the tap staggering method has the potential to increase the reactive power demand drawn from the transmission grid. This thesis also presents an optimal control method for tap stagger to minimise the introduced network loss as well as the number of tap switching operations involved. A genetic algorithm (GA) based procedure has been developed to solve the optimisation problem. The GA method has been compared with two alternative solution approaches, i.e. the rule-based control scheme and the branch-and-bound algorithm. The results indicate that the GA method is superior to the other two approaches. The economic and technical impacts of the tap staggering technique on the transmission system has been studied. In the economic analysis, the associated costs of applying the tap staggering method have been investigated from the perspective of transmission system operator. The IEEE Reliability Test System has been used to carry out the studies, and the results have been compared with the installation of shunt reactors. In the technical studies, the dynamic impacts of tap staggering or reactor switching on transmission system voltages have been analysed. From the results, the tap staggering technique has more economic advantages than reactors and can reduce voltage damping as well as overshoots during the transient states.

Wind tunnel studies of the wake behind a model wind turbine have been made in order to get a better understanding of wake development as well as the possibility to predict the power output from downstream turbines working in the wake of an upstream one. Both two-component hot-wire anemometry as well as particle image velocimetry (PIV) have been used to map the flow field. All three velocity components were measured both for the turbine rotor normal to the oncoming flow as well as with the turbine inclined to the free stream direction (the yaw angle was varied from 0 to 30 degrees). The measurements showed, as expected, a wake rotation in the opposite direction to that of the turbine. A yawed turbine is found to clearly deflect the wake flow to the side showing the potential of controlling the wake position by yawing the turbine. The power output of a yawed turbine was found to vary nearly as the square of the cosine of the yaw angle. The possibility to use active wake control by yawing an upstream turbine was evaluated and was shown to have a potential to increase the power output significantly for certain configurations. An unexpected feature of the flow was that spectra from the time signals showed the appearance of a low frequency fluctuation both in the wake and in the flow outside. This fluctuation was found both with and without free stream turbulence and also with a yawed turbine. The non-dimensional frequency (Strouhal number) was independent of the free-stream velocity and turbulence level but increases with the yaw angle. However the low frequency fluctuations were only observed when the tip speed ratio (or equivalently the drag coefficient) was high. This is in agreement with the idea that the turbine shed structures as a bluff body. It is hypothesized that the observed meandering of wakes in field measurements is due to this shedding.

As the world is increasingly dependent on energy for the economic and social development and China’s Total Net Electricity Generation （TNEG） has remained the highest since 1996 due to its rapid economic growth, it is important to closely examine the operations of China’s electric power market, particularly the State Grid Corporation of China (SGCC) since it is the largest Electric Power Grid Operator (EPGO) in both China and the world. This research has addressed the problem and the urgent needs for the development of a sound framework and methodology for the effective regulation and optimization of the operations and quality management of the SGCC. Based on the critical literature review, the aspects and steps of the solution to the problem have been progressively presented. Firstly, a Country Wealth (CW) curve has been developed to characterize electricity generation in terms of TNEG, with China’s unique position identified. Further, the data has clearly indicated that China’s TNEG has also been closely correlated with the economic growth and the carbon emissions during the 30 years period of 1980-2010. Secondly, compared with the Equilibrium Energy Regulation Model, there are clear deficiencies and problems with the current regulation of China’s electric power market. The improvements in the integration of regulation strategies and the formation of one single effective regulator have been identified and proposed. Thirdly, a uniform regulation structure and framework based on fractal theory and QFD (quality function deployment) has been developed to integrate the existing and future electric power strategies, including smart grid strategy and sustainable development strategy(etc.). Through the use of QFD, the EPGO (SGCC) functions and operations can be prioritized and appropriately designed. Finally, the QFD methodology has been extended to achieve the optimization of quality and service operations given the target cost of the business processes. The methodology can be applied to both business and technical processes of the EPGOs since quality may be interpreted as a total quality involving the needs and expectations of various customers or stakeholders.

The aim of the project was to implement a machine learning model to optimise the power consumption of Ericsson’s Kista data center. The approach taken was to use a Reinforcement Learning agent trained in a simulation environment based on data specific to the data center. In this manner, the machine learning model could find interactions between parameters, both general and site specific in ways that a sophisticated algorithm designed by a human never could. In this work it was found that a neural network can effectively mimic a real data center and that the Reinforcement Learning policy "TD3" could, within the simulated environment, consistently and convincingly outperform the control policy currently in use at Ericsson’s Kista data center.