Model predictive control of DFIG-based wind turbine

Gatavi, E

Model predictive control of DFIG-based wind turbine

Gatavi, E

Permalink

Publication Type:: Thesis
Issue Date:: 2014

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download contents and abstractAdobe PDF (100.81 kB)

Adobe PDF

Download thesisAdobe PDF (1.19 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Gatavi, E
dc.date.accessioned	2014-12-02T05:07:30Z
dc.date.available	2014-12-02T05:07:30Z
dc.date.issued	2014
dc.identifier.uri	http://hdl.handle.net/10453/30358
dc.description	University of Technology, Sydney. Faculty of Engineering and Information Technology.	en_US
dc.description.abstract	Renewable energy as a green source of energy is clean, accessible and sustainable. Due to advanced control, lower cost and government incentives, wind energy has been the largest growth among other renewable sources. With fast growing in the new generation of generators, Doubly Fed Induction Generators (DFIGs) became more popular because of handling a fraction (20-30%) of the total system power which leads to reduce the losses in the power electronic equipment and also their ability in decoupling the control of both active and reactive power. In addition, DFIGs have better behaviour in system stability. Therefore, in this study, the model of one-mass wind turbine with DFIG is represented by a third order model. Model Predictive Control (MPC), as a powerful control method to handle multivariable systems and incorporate constraints, is applied in order to compensate inaccuracies and measurement noise. The optimization problem is recast as a Quadratic Programming (QP) which is highly robust and efficient. Multi-step optimization is introduced to bring the unhealthy voltages as close as possible to the normal operating points so that leads to minimize the changes of the control variables. In order to regulate the power flow between the grid and the generator, it is essential to update reactive power with real power and actual terminal voltage besides reaching maximum reactive power. In this study, the updated control input applies feedback to MPC at each control step by solving a new optimization problem.	en_US
dc.format	Thesis MEng (Research)	en_US
dc.language.iso	en	en_US
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/30358/2/02whole.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.title	Model predictive control of DFIG-based wind turbine	en_US
dc.type	Thesis
utslib.copyright.status	open_access

Abstract:

Renewable energy as a green source of energy is clean, accessible and sustainable. Due to advanced control, lower cost and government incentives, wind energy has been the largest growth among other renewable sources. With fast growing in the new generation of generators, Doubly Fed Induction Generators (DFIGs) became more popular because of handling a fraction (20-30%) of the total system power which leads to reduce the losses in the power electronic equipment and also their ability in decoupling the control of both active and reactive power. In addition, DFIGs have better behaviour in system stability. Therefore, in this study, the model of one-mass wind turbine with DFIG is represented by a third order model. Model Predictive Control (MPC), as a powerful control method to handle multivariable systems and incorporate constraints, is applied in order to compensate inaccuracies and measurement noise. The optimization problem is recast as a Quadratic Programming (QP) which is highly robust and efficient. Multi-step optimization is introduced to bring the unhealthy voltages as close as possible to the normal operating points so that leads to minimize the changes of the control variables. In order to regulate the power flow between the grid and the generator, it is essential to update reactive power with real power and actual terminal voltage besides reaching maximum reactive power. In this study, the updated control input applies feedback to MPC at each control step by solving a new optimization problem.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/30358