Model Predictive Control of a Wind Turbine | Aleksander Gosk
| Abstract | In the era of growing interest in limiting CO2 emission and our dependence on fossil fuels renewable energy sources receive the biggest attention ever. It is predicted [1] that by the year 2035 the use of this kind of energy will triple and wind energy will be the main source of this increase. This work focuses on one of the most common wind energy conversion systems: horizontal axis wind turbine. It's efficiency and longevity relies heavily on the quality of the control approach used. Controller designers are aiming for maximizing the produced electric power for some range of wind speeds and keeping it constant for others. At the same time they have to ensure that the control isn't too aggressive and that it is honouring other constraints that wind turbine is subject to. Those objectives often prove to be opposite in nature and a golden mean - optimal solution - is need. This work presents the control technique that, by it's nature, enables optimal solution of a control problem while honouring constraints that have been imposed upon us by wind turbine's designer - Model Predictive Control. Since control objectives are different for different wind speeds the way in which the controller operate has to change too. Gain and weight scheduling techniques, that will enable smooth shifting between those, so called, operation regions, will be introduced. This approach has the benefit of possibly lowering the stresses that the wind turbine systems are subject too, in comparison with e.g. simple switching between controllers what can be one of the causes of reduced longevity. | Type | Master's thesis [Industrial collaboration] | Year | 2011 | Publisher | Technical University of Denmark, DTU Informatics, E-mail: reception@imm.dtu.dk | Address | Asmussens Alle, Building 305, DK-2800 Kgs. Lyngby, Denmark | Series | IMM-M.Sc.-2011-63 | Note | | Electronic version(s) | [pdf] | Publication link | http://www.imm.dtu.dk/English.aspx | BibTeX data | [bibtex] | IMM Group(s) | Mathematical Statistics |
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