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Torres-Cabrera J, Maldonado-Correa J, Valdiviezo-Condolo M, Artigao E, Martín-Martínez S, Gómez-Lázaro E. A Novel Data-Driven Approach with a Long Short-Term Memory Autoencoder Model with a Multihead Self-Attention Deep Learning Model for Wind Turbine Converter Fault Detection. Applied Sciences. 2024; 14(17):7458. https://doi.org/10.3390/app14177458

Torres-Cabrera, Joel, Jorge Maldonado-Correa, Marcelo Valdiviezo-Condolo, Estefanía Artigao, Sergio Martín-Martínez, and Emilio Gómez-Lázaro. 2024. "A Novel Data-Driven Approach with a Long Short-Term Memory Autoencoder Model with a Multihead Self-Attention Deep Learning Model for Wind Turbine Converter Fault Detection" Applied Sciences 14, no. 17: 7458. https://doi.org/10.3390/app14177458

Torres-Cabrera, J., Maldonado-Correa, J., Valdiviezo-Condolo, M., Artigao, E., Martín-Martínez, S., & Gómez-Lázaro, E. (2024). A Novel Data-Driven Approach with a Long Short-Term Memory Autoencoder Model with a Multihead Self-Attention Deep Learning Model for Wind Turbine Converter Fault Detection. Applied Sciences, 14(17), 7458. https://doi.org/10.3390/app14177458