Abstract
Wind power technology, as the most competitive renewable energy technology, is quickly developing. The wind turbine size is growing and the grid penetration of wind power is increasing rapidly. Recently, the developments on wind power technology pay more attentions on efficiency and reliability. Under these circumstances, research on dc network connection with a novel wind power generator system is presented in this thesis, which mainly consists of two major parts: control of a Switched Reluctance Generator (SRG) system and development of dc-dc converters for a dc network system in a wind farm.
The SRG, which eliminates permanent magnets, brushes, commutators, and coil winding in the rotor, could be a promising wind power generator. It has various desirable features, such as simple and solid structure, easiness of maintenance, fault tolerance, and low cost. These features are suitable for generators in wind turbine systems. However, despite all these advantageous features, the SRG has not been widely employed in wind energy applications. The most renowned technical disadvantages of the SRG are its nonlinearity and high torque ripples, which should be overcome to promote the application of the SRG in wind energy conversion systems.
To overcome the nonlinear characteristics of the SRG, which makes it difficult to achieve satisfactory control performance, a novel SRG speed controller based on self-tuning Fuzzy Logic Control (FLC) is proposed. The proposed controller utilizes the FLC with a self-tuning mechanism to improve the performance of controlling the speed of the SRG. Furthermore, a novel non-unity Torque Sharing Function (TSF) is proposed to minimize the torque ripple over a wide speed range of operation. Unlike the traditional TSFs, the proposed TSF injects deliberate ripple components into the torque reference to compensate the ripples in the actual output torque. The effectiveness and resulting improvement in the performance of both the proposed speed controller and torque minimization technique are demonstrated by simulation results.
The modern power electronic interfaces enable various renewable energy sources, such as Photovoltaic (PV) and wind, to produce dc power directly. In addition, battery-based energy storage systems inherently operate with dc power. Hence, dc network (dc-grid) systems which connect these dc sources and storages directly using dc networks are gaining much attention again. The dc network system has a great potential to outdo the traditional ac systems in many technical challenges and could be highly profitable especially for offshore wind farm applications, where the size and weight of the components are crucial to the entire system costs in terms of substructure requirements, shipping, and installation.
The success of the dc network system is critically dependent upon high-efficient and high-power dc-dc converters. However, no practical high-power (MW-levels) dc-dc converter is commercially available yet. Although lots of research on the dc-dc converters for high-power applications has been carried out to improve their performance, efficiency, and reliability, there still exist several major obstacles, which hamper the further growth of the converter’s power level, such as high power losses in the switching devices and large output filter inductance. To overcome these problems, two novel high-power dc-dc converter topologies are proposed and analyzed: Parallel-Connected Single Active Bridge (PCSAB) dc-dc converter and Double Uneven Power (DUP) converter based dc-dc converter. Various simulation studies and experimental results are presented to verify the feasibility and operational principles of the proposed converters.
Finally, modelling and control of a dc-grid wind farm using one of the proposed dc-dc converters are presented. An average model provides insight into the overall performance of the system. Meanwhile, a switching model provides much detailed information, such as actual peak values of current and voltage ripples in the system. The control of the dc-grid wind farm is developed based on the obtained models and evaluated through various simulation studies. The developed models and control methods are expected to be useful for further studies on the operation of the dc-grid wind farm under various input wind speeds and/or fault conditions.
The SRG, which eliminates permanent magnets, brushes, commutators, and coil winding in the rotor, could be a promising wind power generator. It has various desirable features, such as simple and solid structure, easiness of maintenance, fault tolerance, and low cost. These features are suitable for generators in wind turbine systems. However, despite all these advantageous features, the SRG has not been widely employed in wind energy applications. The most renowned technical disadvantages of the SRG are its nonlinearity and high torque ripples, which should be overcome to promote the application of the SRG in wind energy conversion systems.
To overcome the nonlinear characteristics of the SRG, which makes it difficult to achieve satisfactory control performance, a novel SRG speed controller based on self-tuning Fuzzy Logic Control (FLC) is proposed. The proposed controller utilizes the FLC with a self-tuning mechanism to improve the performance of controlling the speed of the SRG. Furthermore, a novel non-unity Torque Sharing Function (TSF) is proposed to minimize the torque ripple over a wide speed range of operation. Unlike the traditional TSFs, the proposed TSF injects deliberate ripple components into the torque reference to compensate the ripples in the actual output torque. The effectiveness and resulting improvement in the performance of both the proposed speed controller and torque minimization technique are demonstrated by simulation results.
The modern power electronic interfaces enable various renewable energy sources, such as Photovoltaic (PV) and wind, to produce dc power directly. In addition, battery-based energy storage systems inherently operate with dc power. Hence, dc network (dc-grid) systems which connect these dc sources and storages directly using dc networks are gaining much attention again. The dc network system has a great potential to outdo the traditional ac systems in many technical challenges and could be highly profitable especially for offshore wind farm applications, where the size and weight of the components are crucial to the entire system costs in terms of substructure requirements, shipping, and installation.
The success of the dc network system is critically dependent upon high-efficient and high-power dc-dc converters. However, no practical high-power (MW-levels) dc-dc converter is commercially available yet. Although lots of research on the dc-dc converters for high-power applications has been carried out to improve their performance, efficiency, and reliability, there still exist several major obstacles, which hamper the further growth of the converter’s power level, such as high power losses in the switching devices and large output filter inductance. To overcome these problems, two novel high-power dc-dc converter topologies are proposed and analyzed: Parallel-Connected Single Active Bridge (PCSAB) dc-dc converter and Double Uneven Power (DUP) converter based dc-dc converter. Various simulation studies and experimental results are presented to verify the feasibility and operational principles of the proposed converters.
Finally, modelling and control of a dc-grid wind farm using one of the proposed dc-dc converters are presented. An average model provides insight into the overall performance of the system. Meanwhile, a switching model provides much detailed information, such as actual peak values of current and voltage ripples in the system. The control of the dc-grid wind farm is developed based on the obtained models and evaluated through various simulation studies. The developed models and control methods are expected to be useful for further studies on the operation of the dc-grid wind farm under various input wind speeds and/or fault conditions.
| Original language | English |
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| Publisher | |
| Print ISBNs | 978-87-92846-46-4 |
| Publication status | Published - Oct 2014 |