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Abstract
A gain-scheduled feedforward controller employing pseudo-LIDAR wind measurement is designed to augment the baseline feedback controller for wind turbine load reduction during above rated operation. The feedforward controller is firstly designed based on a linearised wind turbine model at one specific wind speed, then expanded for full above rated operational envelope with gain scheduling. The wind evolution model is established using the pseudo-LIDAR measurement data which is generated from Bladed using a designed sampling strategy. The combined feedforward and baseline control system is simulated on a 5MW industrial wind turbine model developed at Strathclyde University. Simulation results demonstrate that the gain scheduling feedforward control strategy can improve the rotor and tower load reduction performance for large wind turbines.
Original language | English |
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Title of host publication | 2016 22nd International Conference on Automation and Computing (ICAC) |
Publisher | IEEE |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Print) | 978-1-86218-132-8 |
DOIs | |
Publication status | Published - 7 Sep 2016 |
Event | 22nd International Conference on Automation and Computing, ICAC 2016 - University of Essex, Colchester, United Kingdom Duration: 7 Sep 2016 → 8 Sep 2016 http://www.cacsuk.co.uk/index.php/conferences |
Conference
Conference | 22nd International Conference on Automation and Computing, ICAC 2016 |
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Abbreviated title | ICAC 2016 |
Country/Territory | United Kingdom |
City | Colchester |
Period | 7/09/16 → 8/09/16 |
Internet address |
Keywords
- wind turbine control
- LIDAR measurement
- feedforward control
- gain scheduling
- load reduction
Fingerprint
Dive into the research topics of 'LIDAR-assisted wind turbine gain scheduling control for load reduction'. Together they form a unique fingerprint.Projects
- 1 Finished
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Supergen Wind Hub
Leithead, B., McDonald, A., McMillan, D., Anaya-Lara, O., Brennan, F., McGregor, P., Attya, A., Campos-Gaona, D., Comerford, D., Hur, S. H., Stock, A. & Yue, H.
19/06/14 → 18/09/19
Project: Research