Detección de fallos en aerogeneradores flotantes mediante redes neuronales usando OpenFAST
- Galeote, Ignacio
- Aimara Andrade, Bryan Alexander
- Esteban, Segundo
- Santos, Matilde
- Ramón Costa Castelló (coord.)
- Manuel Gil Ortega (coord.)
- Óscar Reinoso García (coord.)
- Luis Enrique Montano Gella (coord.)
- Carlos Vilas Fernández (coord.)
- Elisabet Estévez Estévez (coord.)
- Eduardo Rocón de Lima (coord.)
- David Muñoz de la Peña Sequedo (coord.)
- José Manuel Andújar Márquez (coord.)
- Luis Payá Castelló (coord.)
- Alejandro Mosteo Chagoyen (coord.)
- Raúl Marín Prades (coord.)
- Vanesa Loureiro-Vázquez (coord.)
- Pedro Jesús Cabrera Santana (coord.)
Publisher: Servizo de Publicacións ; Universidade da Coruña
ISBN: 9788497498609
Year of publication: 2023
Pages: 144-149
Congress: Jornadas de Automática (44. 2023. Zaragoza)
Type: Conference paper
Abstract
One of the main problems of wind energy is that of production continuity, exacerbated in the case of floating devides due to the added complexity of the environmental loads. Given the intrinsic variability of wind, which leads to irregularities in energy production, it is of particular importance to detect and minimize both the frequency and severity of machine failures or malfunctions. In this work, a 5 MW offshore floating reference turbine has been studied; and failures of various structural elements have been simulated using NREL’s OpenFAST software, using simulation coupling techniques. Then, a neural network has been trained using MATLAB, with the aim of identifying the most suitable sensors to detect these anomalies, as well as their characteristic response that allows a fast and reliable diagnosis of the failure.