131465 20240209155915.0 doi 10.1049/icp.2023.0229 sideral 136825 ART-2023-136825 eng Granado, J. Generation of synthetic examples using generative adversarial networks (GAN) to extend a database of fault signals on power distribution lines 2023 The detection and classification of the type of fault is an essential technique for the improvement of electricity grids due to its potential to improve the reliability of supply and, therefore, its quality. This paper reports a method to obtain an extended database of fault signals in order to use Neural Networks (NN) to process them. The need of a large database for the training process is an inherent need for the right working of a NN. In this type of chaotic nature signals, it is impossible to record enough real ones and, even simulating is near unfeasible task due to the variety of the causes that produces faults events. The proposed solution is to obtain a short database of simulated signals from a real modelled electrical grid and extend this database by means of GAN. This technique simplifies the process to obtain the database of fault signals. Access copy available to the general public Unrestricted info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Herrero, E. Llombart, A. (2023), 1-5 China International Conference on Electricity Distribution, CICED 2161-7481 386871 http://zaguan.unizar.es/record/131465/files/texto_completo.pdf Postprint 2900859 http://zaguan.unizar.es/record/131465/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:131465 articulos driver 2024-02-09-14:30:28 ARTICLE