Abstract
Increasing demands in decentralized power plants have focused attention on Vertical Axis Wind Turbines (VAWTs). However, accessing high range of power from VAWTs is an impediment due to increased loads on the turbine blades. Here, we derive an optimal pitching action that reduces the periodic disturbance on turbine blades of VAWTs without affecting their power production. A control technique called Subspace Predictive Repetitive Control (SPRC) alongwith a LQ Tracker is used for recursive identification to estimate the parameters of VAWT model and further provide an optimal control law accordingly. Basis functions have been used to reduce the dimensionality of the control problem. Simulation results show a great potential of the data-driven SPRC approach coupled with LQ Tracker in reducing the turbine loads on VAWTs.
| Original language | English |
|---|---|
| Title of host publication | Journal of Physics: Conference Series |
| Subtitle of host publication | The Science of Making Torque from Wind (TORQUE 2018) |
| Place of Publication | Bristol, UK |
| Publisher | IOP Publishing |
| Number of pages | 10 |
| Volume | 1037 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | TORQUE 2018: The Science of Making Torque from Wind - Milano, Italy Duration: 20 Jun 2018 → 22 Jun 2018 http://www.torque2018.org/ |
Publication series
| Name | Journal of Physics: Conference Series |
|---|---|
| Publisher | IOP Publishing Ltd. |
| ISSN (Print) | 1742-6588 |
Conference
| Conference | TORQUE 2018 |
|---|---|
| Abbreviated title | TORQUE 2018 |
| Country/Territory | Italy |
| City | Milano |
| Period | 20/06/18 → 22/06/18 |
| Internet address |
Keywords
- Basis Functions
- Lifted Domain
- LQ Tracker
- Subspace Predictive Repetitive Control
- VAWT