Name
Abstract | ||
|---|---|---|
The control of Variable Speed Wind Turbines (VSWT) to achieve optimal balance of power generation stability and rotor angular speed is impeded by the non-linear dynamics of the turbine-wind interaction and sudden changes of wind direction and speed. Conventional approaches to design VSWT controllers are not adaptive. However, the wind shear phenomenon introduces a strongly non-stationary environment that requires adaptive control approaches with minimal human intervention, i.e. very little supervision of the adaptation process. Reinforcement Learning (RL) allows minimally supervised learning. Specifically, Actor-Critic is designed to deal with continuous valued state and action spaces. In this paper we apply an Actor-Critic RL architecture to improve the adaptation of the conventional VSWT controllers to changing wind conditions. Simulation results on a benchmark VSWT model under strongly changing wind conditions show that Actor Critic RL approach with functional approximation provide great enhancement over state-of-the-art VSWT controllers. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3233/ICA-160531 | INTEGRATED COMPUTER-AIDED ENGINEERING |
Keywords | Field | DocType |
Wind-turbine, control, reinforcement, learning, adaptive | Control theory,Computer science,Simulation,Variable speed wind turbine,Reinforcement learning | Journal |
Volume | Issue | ISSN |
24 | 1 | 1069-2509 |
Citations | PageRank | References |
1 | 0.40 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Borja Fernández-Gauna | 1 | 31 | 5.82 |
| Unai Fernandez-Gamiz | 2 | 1 | 0.40 |
| Manuel Graña | 3 | 1367 | 156.11 |