000131465 001__ 131465
000131465 005__ 20240209155915.0
000131465 0247_ $$2doi$$a10.1049/icp.2023.0229
000131465 0248_ $$2sideral$$a136825
000131465 037__ $$aART-2023-136825
000131465 041__ $$aeng
000131465 100__ $$aGranado, J.
000131465 245__ $$aGeneration of synthetic examples using generative adversarial networks (GAN) to extend a database of fault signals on power distribution lines
000131465 260__ $$c2023
000131465 5060_ $$aAccess copy available to the general public$$fUnrestricted
000131465 5203_ $$aThe 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.
000131465 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000131465 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000131465 700__ $$aHerrero, E.
000131465 700__ $$aLlombart, A.
000131465 773__ $$g(2023), 1-5$$tChina International Conference on Electricity Distribution, CICED$$x2161-7481
000131465 8564_ $$s386871$$uhttps://zaguan.unizar.es/record/131465/files/texto_completo.pdf$$yPostprint
000131465 8564_ $$s2900859$$uhttps://zaguan.unizar.es/record/131465/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000131465 909CO $$ooai:zaguan.unizar.es:131465$$particulos$$pdriver
000131465 951__ $$a2024-02-09-14:30:28
000131465 980__ $$aARTICLE