000086188 001__ 86188
000086188 005__ 20200716101611.0
000086188 0247_ $$2doi$$a10.3390/en12132485
000086188 0248_ $$2sideral$$a113455
000086188 037__ $$aART-2019-113455
000086188 041__ $$aeng
000086188 100__ $$aBarrios, Sonia
000086188 245__ $$aPartial discharge classification using deep learning methods—survey of recent progress
000086188 260__ $$c2019
000086188 5060_ $$aAccess copy available to the general public$$fUnrestricted
000086188 5203_ $$aThis paper examines the recent advances made in the field of Deep Learning (DL) methods for the automated identification of Partial Discharges (PD). PD activity is an indication of the state and operational conditions of electrical equipment systems. There are several techniques for on-line PD measurements, but the typical classification and recognition method is made off-line and involves an expert manually extracting appropriate features from raw data and then using these to diagnose PD type and severity. Many methods have been developed over the years, so that the appropriate features expertly extracted are used as input for Machine Learning (ML) algorithms. More recently, with the developments in computation and data storage, DL methods have been used for automated features extraction and classification. Several contributions have demonstrated that Deep Neural Networks (DNN) have better accuracy than the typical ML methods providing more efficient automated identification techniques. However, improvements could be made regarding the general applicability of the method, the data acquisition, and the optimal DNN structure
000086188 536__ $$9info:eu-repo/grantAgreement/EC/H2020/676042/EU/Metrology Excellence Academic Network for Smart Grids/MEAN4SG$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 676042-MEAN4SG
000086188 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000086188 590__ $$a2.702$$b2019
000086188 591__ $$aENERGY & FUELS$$b63 / 112 = 0.562$$c2019$$dQ3$$eT2
000086188 592__ $$a0.635$$b2019
000086188 593__ $$aControl and Optimization$$c2019$$dQ2
000086188 593__ $$aElectrical and Electronic Engineering$$c2019$$dQ2
000086188 593__ $$aRenewable Energy, Sustainability and the Environment$$c2019$$dQ2
000086188 593__ $$aEnergy Engineering and Power Technology$$c2019$$dQ2
000086188 593__ $$aFuel Technology$$c2019$$dQ2
000086188 593__ $$aEnergy (miscellaneous)$$c2019$$dQ2
000086188 655_4 $$ainfo:eu-repo/semantics/review$$vinfo:eu-repo/semantics/publishedVersion
000086188 700__ $$0(orcid)0000-0003-3431-5863$$aBuldain, David$$uUniversidad de Zaragoza
000086188 700__ $$0(orcid)0000-0002-4133-7553$$aComech, María Paz$$uUniversidad de Zaragoza
000086188 700__ $$aGilbert, Ian
000086188 700__ $$aOrue, Orue
000086188 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000086188 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000086188 773__ $$g12, 13 (2019), 2485 [16 pp.]$$pENERGIES$$tEnergies$$x1996-1073
000086188 8564_ $$s3533701$$uhttps://zaguan.unizar.es/record/86188/files/texto_completo.pdf$$yVersión publicada
000086188 8564_ $$s109110$$uhttps://zaguan.unizar.es/record/86188/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000086188 909CO $$ooai:zaguan.unizar.es:86188$$particulos$$pdriver
000086188 951__ $$a2020-07-16-09:57:18
000086188 980__ $$aARTICLE