Currently there is strong interest in the deployment of renewable energy sources such as wind, solar and hydro energy. This is driven in part due to the negative consequences of burning fossil fuels, such as health issues and climate change. To optimize the energy extracted in a wind farm the turbines require some control algorithm. Today, wind turbines that are part of a wind farm do not take neighbouring turbines into account when determining their control settings. This results in greedy control, where each turbine tries to align itself with the dominant wind direction and optimize its energy production using generator torque control and pitch control. During operation, each turbine creates a volume of slow-moving turbulent air behind its rotor, which is called a wake. The issue with greedy control is that a turbine does not take into account where its wake will end up with respect to downwind turbines. By misaligning a turbine with the wind direction its trust can displace the wake. This will cause an efficiency loss on the misaligned turbine, but it can be used to increase the efficiency of a downwind turbine, leading to an increase in net power production. We know that a controller that uses yaw optimization can optimize the power production in a small scale experimental setup [Campagnolo et al., 2016]. In this particular research a line of three turbines produced 15% extra power compared to greedy control. Wake redirection can be thought of as an attempt to efficiently mix the slower moving air in the wake with the faster stream surrounding it. This allows more of the total energy in the free stream to be extracted by any given wind farm. Yaw redirection has the potential to mix in the high velocity air that could otherwise pass through the empty space between the turbines. Tilt redirection has the potential to more efficiently mix the high velocity air that would otherwise pass over the wind farm into the air that hits the rotors [Annoni et al., 2017]. This work focuses on the development and implementation of a wake model that can be used to predict the effects of rotor tilting on the wake. We started with the FLOw Redirection and Induction in Steady state (FLORIS) model as described in [Gebraad et al., 2014]. This model can be used to estimate the power production of a wind farm. It models the wake intensity and position and combines wakes when they overlap. The power production and wake characteristics of each turbine are predicted using three sub-models. There is one model for the wake intensity and its velocity profile, one to estimate wake deflection and one to determine how wakes are added to each other. In current practice, the wake deflection is modeled only in a horizontal plane, it can be driven by rotor yaw. In this work, the deflection model will be extended in such a way that the effects of rotor tilting on the wake position can be modeled. Tuning a reduced order model such as FLORIS is notoriously difficult. In the FLORIS model, as described in [Gebraad et al., 2014] there are twelve hand tuned parameters. An important part of this research was attempting to simplify the tuning problem by making a robust parameter tuning procedure. A sensitivity analysis of the tuning parameters on the predicted power signals by FLORIS was performed. This analysis was used to identify situations where a subset of the model parameters is responsible for the variance in the predicted power production. Such situations with specific sensitivity were identified, but I lacked time and resources to fully leverage these and accurately tune the tilt extended model. We stuck with the original nominal parameters of the different parts constituting FLORIS to conduct a case study. The case study compared predicted power increases by FLORIS with high fidelity simulations in two different Large Eddy Simulation (LES) packages. The case study was conducted by trying to optimize the power production of a small wind farm containing six turbines. The wind turbines were positioned in a two by three grid and the wind direction was aligned along the three turbines. The FLORIS model was used to optimize the yaw angles, tilt angles and both of them simultaneously for this layout. This led to four sets of control settings, the baseline case with greedy control and three optimized sets. We found that the FLORIS model strongly overestimated the power gain caused by turbine tilt. The main reason for this over-estimation seems to be that FLORIS currently has no implementation of the ground. In effect, the wakes in FLORIS can simply disappear into the ground. The case where only the yaw angles are optimized matched the high fidelity simulations to a higher degree confirming the work done in [Bastankhah and Port´e-Agel, 2016]. In conclusion, tilt control seems to have the potential to extract more energy from a certain atmospheric region. This is postulated because the air that gets forced on to rotors using tilt control would otherwise have passed unused over a wind farm. However, the model proposed in this thesis is insufficient to analyze the possible energy gains because it overestimates the effect of turbine tilting. This biggest problem with the FLORIS model is that it lacks a method for modeling the interaction between the ground and a wake. For future research the main priority should be implementing a solution to that problem.

The power generated from wind is not synchronized to the electrical frequency of the power grid. Grid balancing services must be assured when the output from a wind farm is integrated into the electrical grid to avoid the risk of blackouts. Active Power Control (APC) methods are employed to provide grid balancing ancillary services such as frequency control. One objective of APC is to have the total power generated from a wind farm, track the power demand requirements obtained from the utility grid. To achieve this objective, the wind farm should be able to operate below their maximum power production capacity i.e, in derating mode. This implies that the turbines in the wind farm should also be derated. The presence of wakes in the wind farm results in the downstream turbines to experience reduced wind speed and increased turbulence. The earlier works on APC for wind farms revealed the need for a closed-loop wind farm control strategy to combat the effect of wake turbulence. The presence of wakes posed several challenges on obtaining the estimate of the available power at every turbine on a time scale of seconds. Yet, some model-free algorithms were dependent on the estimation of available power at every turbine in the wind farm. This, leads us to the question, “Can a wind farm controller be developed to provide APC for waked wind farms, where the setpoint selection and distribution are made without estimating the available power at each turbine?” To explore the answer to this question, a single wind turbine power tracking control algorithm is developed as the first step. This tracking algorithm does not depend on the estimation of available power. The proposed algorithm makes the turbine operate on two different operating modes namely, the perfect tracking mode and greedy/boosting mode. The algorithms were developed in a way that they can be integrated with the existing torque and pitch controllers of the turbine. Following this, a closed-loop wind farm control strategy has been developed. The closed-loop wind farm controller takes the total power generated from the wind farm as the feedback signal. Based on the operating mode of the individual turbines, the wind farm controller coordinates and distributes the total power reference signal as individual power set-points to the respective wind turbines. The performance of the closed loop wind farm controller was evaluated for a 9-turbine case in SOWFA simulation for four different scenarios. The scenarios differed from each other based on the way the turbines in the wind farm are derated and the individual set points to the turbines are distributed by the wind farm controller. Simulation results showed that the scenario in which the upstream turbines are derated more than the downstream turbines, the tracking performance was better compared to the other scenarios. The damage equivalent loads experienced by the tower base of the individual turbines were also calculated and each of the scenario resulted in different loading patterns. Recommendations are also provided to extend this work and perform further research.

At large oceans depths (5000m) manganese nodules are formed, these are rock shaped objects that contain various rare earth metals, Royal IHC is currently developing equipment to bring these nodules to the shore. The nodules will be transported to the water surface using a Vertical Transport System (VTS). In the VTS insight of the location of solid concentrations is required: This enables anticipation of the coming flow at the vessel, it is required for controlling the pumps, it will indicate where plugs are likely to be formed and furthermore it will indicate if the aimed production is achieved. For the vertical transport system it has been proposed to measure the volumetric concentration of solids inside the booster stations located every 1000m and predict the propagation of solids in between these measurements, it was found that this configuration has the disadvantage that measurement error in the booster stations results in an error over the whole length of a riser section. An unknown parameter of the slurry flow is the particle diameter, this parameter influences the transport velocity of the solids. These two topics resulted in the following question: "How can the observation of solid concentrations inside a riser be improved?". In order to evaluate this research question an observer is designed for a scaled test setup of the VTS, on this setup designed improvements will be tested. Started is with an observability analysis, it was found that pressure difference measurements over a large distance of the riser will increase the observability of the system. In order to apply the observer to the test setup it has been investigated how the pressure difference measurements translate into a measured concentration, it was found that the pressure drop created by the wall friction of the mixture can be approximated with liquid wall friction. The Ensemble Kalman Filter was used to observer the concentration through the riser. An addition of the observer is an estimator for the particle diameter: A proposal for this observer is to adapt the particle diameter by using a proportional integral of the lag found between the concentration estimate over a large distance and the concentration measurement at that point. The observer has been applied to measurements of a scaled riser section of 140m. It was found that by using a pressure difference measurement over the whole section of a riser, the concentration observation at the top of the riser can by significantly improved. Using the observer it has been enabled to distinguish different mixtures. There are errors in the outcome of the particle diameter estimate due to the fact that at the conditions of the test setup were not optimal, however it has been proven that by applying the particle diameter observer an improvement can be seen in the concentration observation. A sensitivity of the observer was found to be the relation of the pressure difference measurement to the wall roughness of the riser, the effect of this parameter needs to be accounted for by periodically re-determining the wall roughness.

In research, the overall power production in a wind farm is typically increased by employing model-based wind farm control. A controller, in an open-loop setting, operates based on the velocity of the wind flow, predicted by a wind farm model. For a controller to achieve the desired level of power production, a wind farm model has to be accurate and computationally tractable. Generally, high-fidelity wind farm models are accurate but are computationally complex, making real-time control infeasible. This issue can, however, be addressed by employing closed-loop approach. In this approach, low- or medium-fidelity wind farm models, which are computationally tractable, are used, and their accuracy Is improved by employing an estimator. In the previous researches, a centralized estimation approach was employed. In this framework, a single estimator is employed for estimating all the states (second-to-second wind field at turbine hub height) in a wind farm. Simulation results show that the accuracy of an open-loop model can be improved. However, the problem is the state size of the wind farm models. This leads to the objective of the thesis, which is “Can the accuracy of a wind farm model be improved while maintaining computational tractability?”. In this thesis, distributed estimation is proposed as a solution to this problem. The basic idea behind distributed estimation is to distribute a wind farm into a number of small spatial domains (subsystems), and define a wind farm model for each of these subsystems to independently predict the wind flow in their respective spatial domain. For estimation, each sub-system employs an estimator to independently estimate their respective states, in parallel. In this thesis, based on the extent to which the states are estimated (measurement-update), model distribution, and size of the subsystems, four types of distributed architectures are devised, using the medium-fidelity model WindFarmSimulator (WFSim). Simulations show negligible loss in performance, and at the same time, the time taken for each iteration decreases drastically, making it computationally tractable. In conclusion, distributed architectures are capable of improving the accuracy of the open-loop wind farm models, to the same level of accuracy offered by the centralized architecture, while maintaining computational tractability. Additionally, application of these distributed architectures for controller design will be a scope for future research in this topic.