Predictive control of wind turbines with storage

Sharma, Rahul, Yan, Ruifeng and Kearney, Michael (2013). Predictive control of wind turbines with storage. In: Proceedings of the 3rd Australian Control Conference 2013. AUCC 2013: Third Annual Australian Control Conference, Perth, WA, Australia, (177-182). 4-5 November, 2013. doi:10.1109/AUCC.2013.6697269

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Author Sharma, Rahul
Yan, Ruifeng
Kearney, Michael
Title of paper Predictive control of wind turbines with storage
Conference name AUCC 2013: Third Annual Australian Control Conference
Conference location Perth, WA, Australia
Conference dates 4-5 November, 2013
Convener National Committee for Automation, Control and Instrumentation
Proceedings title Proceedings of the 3rd Australian Control Conference 2013
Journal name 2013 3rd Australian Control Conference, AUCC 2013
Place of Publication Barton, ACT, Australia
Publisher Engineers Australia
Publication Year 2013
Sub-type Fully published paper
DOI 10.1109/AUCC.2013.6697269
Open Access Status
ISBN 9781479924974
Start page 177
End page 182
Total pages 6
Collection year 2014
Language eng
Formatted Abstract/Summary
The large-scale use of wind power generation continues to be hindered due to its intermittency. Among the potential solutions to this problem, the adoption of battery-based storage systems is widely seen as inevitable. The aim of this paper is to develop a real-time model-based optimisation approach for the coordinated control of a wind turbine equipped with battery storage. First, the mathematical model of the wind turbine-battery system is systematically reduced using singular perturbation theory. Then, the obtained reduced-order model is utilised in the control system development. The control system is devised using a real-time implementable version of model predictive control whereby the nonlinear dynamics are linearised at each sampling instant to simultaneously overcome the computational issues due to nonlinear optimisation and performance degradation issues due to linearisation at only one operating point. The effectiveness of the proposed controller in reducing wind intermittency through the optimal management of the battery storage is demonstrated using simulation studies involving real wind-data from an Australian wind farm.
Keyword Wind turbine control
Battery storage
Model predictive control
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Presented during Interactive Session MoIT4 "Control Applications-II" as Paper MoIT4.2

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Created: Wed, 04 Dec 2013, 14:59:22 EST by Rahul Sharma on behalf of School of Information Technol and Elec Engineering