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Convolutional neural network for wind turbine failure classification based on SCADA data

Author
Puruncajas, B.; Alava, W.; Encalada-Dávila, Á.; Tutivén, C.; 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
316-21-1
Last page
316-21-5
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/icrepq21/316-21-puruncajas.pdf Open in new window
Abstract
As a renewable energy source and an alternative to fossil fuels, the wind power industry is growing rapidly. However, due to harsh weather conditions, wind turbines (WT) still face many failures that raise the price of energy produced and reduce the reliability of wind energy. Hence, the use of reliable monitoring and diagnostic systems of WTs is of great importance. Operation and maintenance expenses represent 30% of the total cost of large wind farms. The installation of offshore and remote wi...
Keywords
Convolutional neural network, FAST, Fault detection and classification, SCADA data, Wind turbine
Group of research
CoDAlab - Control, Dynamics and Applications

Participants

  • Puruncajas Maza, Bryan  (author and speaker )
  • Alava, Winter  (author and speaker )
  • Encalada-Dávila, Ángel  (author and speaker )
  • Tutivén Gálvez, Christian  (author and speaker )
  • Vidal Segui, Yolanda  (author and speaker )