Project

Supported by DHRTC-DTU via Smart Water Flooding Flagship Programme. Two PhD positions. The objective of the SWTS is to develop a smart water management system that addresses both optimal operational performance and process development/design, by employing the advanced control and big data analytics technologies. This work will focus on innovative analysis, design and development of both Produced Water Treatment (PWT) and Injection Water Treatment (IWT) for offshore enhanced oil recovery using advanced water-flooding technology.

Floating offshore wind turbines (FOWT) is a new technology, which is still in its developing stage. FOWT could be the solution in order to increase the possible construction areas, as they are more suitable for deeper waters. But the downside is that a floating foundation introduces additional dynamics to the system, which could lead to complex constructions and thereby decrease their cost/effectiveness. If the FOWT control systems take these dynamics into account it could minimize the impact of these and thereby increase the advancement

of FOWTs. Therefore in this project it is sought to develop a physically scaled model of a real wind turbine, which is able to be controlled similar to real wind turbine systems, this includes generator torque control and blade pitching control. The physical model must be constructed in order to test and verify these controlling methods. In this project the scaled nacelle of a wind turbine is designed and constructed, together with the power electronics. It is a 1:35 scaled model of the NREL 5 MW reference wind turbine. Furthermore, blades are designed and constructed in order to match the scaled thrust force of the reference wind turbine. The dynamic models of the subsystems of the wind turbine are developed and controllers for them are designed. The controller's impact is simulated in simulink models of the subsystems.

The project aims to develop wind farm models based on data and artificial intelligence algorithms. The model and data will support the design of intelligent control algorithms for wind farms. This modeling method is used to solve the problem that existing models cannot be used for actual wind farm control. The model uses machine learning models to learn high-fidelity model data to improve the performance of low-fidelity models. So as to achieve the balance between the fidelity required by the control algorithms and the computational cost. The wind farm control algorithm based on this model aims to improve the power production and turbine life of the total farm by intelligent wake redirection. The wind power industry will also benefit from the development of artificial intelligence algorithms. Reinforcement learning is used to design intelligently optimized controllers for wind farms.

This project enables Danish participation in IEA Wind Task 44: Farm Flow Control. The focus is on control strategies to mitigate wake effects in wind farms. The purpose of IEA Wind Task 44 is to coordinate international research in the field of wind field control inside wind farms. The technology used for this task covers a wide range, but focuses primarily on control algorithms and strategies and how they are transferred to real-world operational improvements.

The intention is to bring together ongoing research results as well as best industry practice, create an overview of control strategies and algorithms and investigate how uncertainties affect the performance and potential for implementation of wind farm control.
The result is guidance for the wind industry and researchers on the current control algorithms, requirements, barriers to adoption, future directions and expected benefits of wind farm control.

To transfer energy from collected offshore wind farms over a long distance, HVDC transmission is preferred over HVAC in terms of efficiency and economy. Several multi-stage configurations have been proposed in the literature. However, the multi-stage configuration generally results in a large size due to a large number of conversion stages, relatively high cost, and low efficiency and power density. Also, the independent control of several converters and communication among the sources make the system complex. To overcome these disadvantages, multi-port modular DC/DC topologies have been suggested. Multiport converters are highly non-linear MIMO systems with many control variables. Also, the coupling between the control variables makes modeling and control system design complicated. Despite such complexity, advanced control techniques have not been comprehensively studied. Moreover, most controller design work on multiport converters has not considered the uncertainties of the converter model. In this Ph.D. study, a robust controller is implemented for multi-port modular DC/DC converter for offshore wind farms application.

HVDC offshore wind farms with MVDC power collection have recently aroused researchers' interest as these systems offer lower losses and fabrication expenses. Numerous potential MVDC converters could be used in the power collection stage of offshore wind farms; however, when it comes to the technology level, these DC/DC converters are still immature since no substantial studies concerning their control exist. Thus, this Ph.D. project aims to address the research gap to enhance the performance as well as the efficiency of an MVDC converter. The novel switching and control technique proposed in this project together with the significant features of wide bandgap switches provide the condition based on which the MVDC converter could operate at higher switching frequencies than what is already possible. Hence, the controlled MVDC converter will be smaller in size and lighter in weight compared to the conventional ones which reduces the LCOE and provides better possibilities for modularity.

The aim is to obtain knowledge of nonlinear loads which could cause unwanted dynamics or stability issues for the DC-microgrid. This leads to the investigation of the nonlinear loads: Constant Power Loads and Reversible Solid Oxide Cells.
The design of a suitable DC-DC converters and control systems are investigated to fulfill performance requirements and mitigate stability issues.

The purpose of the project is to develop a gyroelectric energy conversion unit for wave energy. In order to demonstrate the technology under realistic conditions, a series of experimental tests will be carried out at the Nissum Bredning Test Station on a 5 kW unit.
The following main activities will be held:
Continuation of wave basin tests on an existing prototype at AAU. Including determination of the absorbed power at different standard sea conditions. Tests with irregular waves to optimize energy absorption under realistic conditions.
Design and manufacture of a 5 kW PTO unit. In the design and in the choice of manufacturing methods, emphasis will be placed on using standard components and manufacturing methods that can also be used in a possible production of a full-scale PTO unit (15, 30 and 50 kW).
Testing and demonstration of a 5 kW PTO unit at the Nissum Bredning Test Station. Over a period of approx. 10 months from August 2015 to June 2016, a series of tests will be carried out with the PTO unit mounted to the test station platform approx. 140 m from shore.
Preparation of a measurement program data processing for the tests at AAU, as well as the testing at Nissum Bredning.
Contact with wave power developers. In the final part of the project, a number of Danish and foreign wave power developers will be contacted with a view to starting an end-user dialogue with 2-3 wave power developers.

The long-term goals of this task are:
1. To assess the accuracy and establish confidence in the use of numerical WEC models
2. To determine a range of validity of existing computational modeling tools
3. To identify uncertainty related to simulation methodologies in order to:
a. Reduce risk in technology development
b. Improve WEC energy capture estimates
c. Improve loading estimates
d. Reduce uncertainty in LCOE models
4. To define future research and develop methods of verifying and validating the different types of numerical models required depending on the conditions

Turkey is one of the fastest-growing energy markets in the world, with an annual 8% increase in energy demand. By the end of 2018, the total installed capacity and electricity production of Turkey was 88.5 GW and 300.7 TWh, respectively. Nowadays, more than 70% of all electricity production is supplied by fossil resources, and almost 30% of all electricity production comes from renewables, mainly hydro, while wind constitutes only 6.6% of the total electricity mix.

The wind and solar energy rate in total consumption are planned to be increased by at least 30% in the coming five years according to the 2023 vision plan of Turkey. However, due to the intermittent nature of wind energy, large-scale wind power integration may pose some serious challenges to Turkey's power system. Therefore, planning analysis and designing efforts are required to ensure the smooth, secure and reliable operation of Turkey's power system and electricity markets considering large-scale wind power integration. WindFlag aims to solve relevant challenges of large scale OWPP deployment and integration into the Turkish grid, such as extreme weak-grid situations, islanding conditions, and large harmonics and resonances.