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Posts
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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
Detection and classification of fault types in distribution lines by applying contrastive learning to GAN encoded time-series of pulse reflectometry signals
Published in IEEE Access, 2022
Recommended citation: J. G. Fornás, E. H. Jaraba, A. L. Estopiñan and J. Saldana, "Detection and Classification of Fault Types in Distribution Lines by Applying Contrastive Learning to GAN Encoded Time-Series of Pulse Reflectometry Signals," in IEEE Access, vol. 10, pp. 110521-110536, 2022
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Modeling and Simulation of Time Domain Reflectometry Signals on a Real Network for Use in Fault Classification and Location
Published in IEEE Access, 2023
Recommended citation: J. G. Fornás, E. H. Jaraba, H. Bludszuweit, D. C. García and A. L. Estopiñan, "Modeling and Simulation of Time Domain Reflectometry Signals on a Real Network for Use in Fault Classification and Location," in IEEE Access, vol. 11, pp. 23596-23619, 2023
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TransSiamese: A Transformer-Based Siamese Network for Anomaly Detection in Time Series as Approach for Fault Location in Distribution Grids
Published in IEEE Access, 2023
Recommended citation: J. G. Fornás, E. H. Jaraba and A. L. Estopiñan, "TransSiamese: A Transformer-Based Siamese Network for Anomaly Detection in Time Series as Approach for Fault Location in Distribution Grids," in IEEE Access, vol. 11, pp. 103431-103451, 2023
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talks
Deep learning applied to fault signal processing in power grids
Published:
Automatic detection, classification and location of power line faults is a crucial process in the electrical power industry. It consists of quickly identifying and locating any type of fault or interruption on a power line such as short circuits, earth faults or faults in the electrical distribution system.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
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