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Wind turbine multi-fault detection based on SCADA data via an autoencoder

Author
Encalada-Dávila, Á.; Tutivén, C.; Puruncajas, B.; Vidal, Y.
Type of activity
Presentation of work at congresses
Name of edition
International Conference on Renewable Energies and Power Quality
Date of publication
2021
Presentation's date
2021-07
Book of congress proceedings
19th International Conference on Renewable Energies and Power Quality - Proceedings book
First page
325-21-1
Last page
325-21-6
Project funding
DPI2017-82930-C2-1-R DESARROLLO Y VALIDACION DE SISTEMAS DE MONITORIZACION INTELIGENTE, ESTRATEGIAS DE CONTROL DEL PITCH Y DE AMORTIGUACION ESTRUCTURAL PARA AEROGENERADORES OFFSHOREFLOTANTES
Grup de recerca acreditat: Control, Modelització, Identificació i Aplicacions
Repository
https://www.icrepq.com/icrepq21/325-21-tutiven.pdf Open in new window
Abstract
Nowadays, wind turbine fault detection strategies are settled as a meaningful pipeline to achieve required levels of effi- ciency, availability, and reliability, considering there is an increasing installation of this kind of machinery, both in onshore and offshore configuration. In this work, it has been applied a strategy that makes use of SCADA data with an increased sampling rate. The employed wind turbine in this study is based on an advanced benchmark, established by the National Renewable...
Keywords
Autoencoder, Multi-fault detection, Normality model, SCADA data, Wind turbine
Group of research
CoDAlab - Control, Dynamics and Applications

Participants