Model predictive control for advanced multilevel power converters in smart-grid applications
In the coming decades, electrical energy networks will gradually change from a traditional passive network into an active bidirectional one using concepts such as these associated with the smart grid. Power electronics will play an important role in these changes. The inherent ability to control...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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2014
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| Online Access: | https://eprints.nottingham.ac.uk/27742/ |
| _version_ | 1848793427616989184 |
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| author | Tarisciotti, Luca |
| author_facet | Tarisciotti, Luca |
| author_sort | Tarisciotti, Luca |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | In the coming decades, electrical energy networks will gradually change from a traditional passive network into an active bidirectional one using concepts such as these associated with the smart grid.
Power electronics will play an important role in these changes. The inherent ability to control power flow and respond to highly dynamic network will be vital. Modular power electronics structures which can be reconfigured for a variety of applications promote economies of scale and technical advantages such as redundancy. The control of the energy flow through these converters has been much researched over the last 20 years.
This thesis presents novel control concepts for such a structure, focusing mainly on the control of a Cascaded H-Bridge converter, configured to function as a solid state substation. The work considers the derivation and application of Dead Beat and Model Predictive controllers for this application and scrutinises the technical advantages and potential application issues of these methodologies. Moreover an improvement to the standard Model Predictive Control algorithm that include an intrinsic modulation scheme inside the controller and named Modulated Model Predictive Control is introduced.
Detailed technical work is supported by Matlab/Simulink model based simulations and validated by experimental work on two converter platforms, considering both ideal and non-ideal electrical network conditions. |
| first_indexed | 2025-11-14T19:00:08Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-27742 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T19:00:08Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-277422025-02-28T11:32:18Z https://eprints.nottingham.ac.uk/27742/ Model predictive control for advanced multilevel power converters in smart-grid applications Tarisciotti, Luca In the coming decades, electrical energy networks will gradually change from a traditional passive network into an active bidirectional one using concepts such as these associated with the smart grid. Power electronics will play an important role in these changes. The inherent ability to control power flow and respond to highly dynamic network will be vital. Modular power electronics structures which can be reconfigured for a variety of applications promote economies of scale and technical advantages such as redundancy. The control of the energy flow through these converters has been much researched over the last 20 years. This thesis presents novel control concepts for such a structure, focusing mainly on the control of a Cascaded H-Bridge converter, configured to function as a solid state substation. The work considers the derivation and application of Dead Beat and Model Predictive controllers for this application and scrutinises the technical advantages and potential application issues of these methodologies. Moreover an improvement to the standard Model Predictive Control algorithm that include an intrinsic modulation scheme inside the controller and named Modulated Model Predictive Control is introduced. Detailed technical work is supported by Matlab/Simulink model based simulations and validated by experimental work on two converter platforms, considering both ideal and non-ideal electrical network conditions. 2014-05-05 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/27742/1/Thesis_ToPrint.pdf Tarisciotti, Luca (2014) Model predictive control for advanced multilevel power converters in smart-grid applications. PhD thesis, University of Nottingham. Predictive control electric current converters smart power grids |
| spellingShingle | Predictive control electric current converters smart power grids Tarisciotti, Luca Model predictive control for advanced multilevel power converters in smart-grid applications |
| title | Model predictive control for advanced multilevel power converters in smart-grid applications |
| title_full | Model predictive control for advanced multilevel power converters in smart-grid applications |
| title_fullStr | Model predictive control for advanced multilevel power converters in smart-grid applications |
| title_full_unstemmed | Model predictive control for advanced multilevel power converters in smart-grid applications |
| title_short | Model predictive control for advanced multilevel power converters in smart-grid applications |
| title_sort | model predictive control for advanced multilevel power converters in smart-grid applications |
| topic | Predictive control electric current converters smart power grids |
| url | https://eprints.nottingham.ac.uk/27742/ |