Wind Turbine Pitch Control with an RBF Neural Network
-
Jesus Enrique Sierra-García [1] ; Matilde Santos [2]
-
[1] Universidad de Burgos
Universidad de Burgos
Burgos, España
-
[2] Universidad Complutense de Madrid
Universidad Complutense de Madrid
Madrid, España
-
- Localización: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020 / coord. por Álvaro Herrero Cosío, Carlos Cambra Baseca, Daniel Urda Muñoz, Javier Sedano Franco, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2021, ISBN 978-3-030-57802-2, págs. 397-406
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
-
Resumen
There are many control challenges in wind turbines: controlling the generator speed, blade angle adjustment (pitch control), and the rotation of the entire wind turbine (yaw control). In this work a neuro-control strategy is proposed to control the pitch angle of the wind turbine. The control architecture is based on an RBF neural network and an on-line learning algorithm. The neural network is not pre-trained but it learns from the system response (power output) in an unsupervised way. Simulation results on a small wind turbine show how the controller is able to stabilize the power output around the rated value for different wind ranges. The controller has been compared with a PID regulator with encouraging results.
