Wind Turbine Pitch Control First Approach Based on Reinforcement Learning

  1. J. Enrique Sierra-García 1
  2. Matilde Santos 2
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

  2. 2 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

Libro:
Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings
  1. Cesar Analide (ed. lit.)
  2. Paulo Novais (ed. lit.)
  3. David Camacho (ed. lit.)
  4. Hujun Yin (ed. lit.)

Editorial: Springer International Publishing AG

ISBN: 978-3-030-62362-3,  978-3-030-62361-6,  978-3-030-62364-7,  978-3-030-62365-4

Año de publicación: 2020

Título del volumen: Part II

Volumen: 2

Páginas: 260-268

Congreso: Intelligent Data Engineering and Automated Learning – IDEAL (21. 2020. Guimarães)

Tipo: Aportación congreso

DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

The control strategy defined for a wind turbine (WT) aims to achieve the highest energy efficiency and at the same time to ensure safe operation under all wind conditions. The goal of the pitch control of a WT is to stabilize the output power around its nominal (rated) value by means of the position of the rotor blades with respect to the wind. In this work, a pitch control strategy based on reinforcement learning (RL) is proposed. The control system consists of a state estimator, a reward mechanism, a policy table and policy update algorithm. Different reward strategies and policy update algorithms for the RL controller have been tested and compared with a PID regulator. The proposed controller stabilizes the output power of the wind turbine around the rated power more accurately and with smaller overshoot than the traditional one.

Fuente de los datos: Dialnet