86188 20200716101611.0 doi 10.3390/en12132485 sideral 113455 ART-2019-113455 eng Barrios, Sonia Partial discharge classification using deep learning methods—survey of recent progress 2019 Access copy available to the general public Unrestricted This 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 info:eu-repo/grantAgreement/EC/H2020/676042/EU/Metrology Excellence Academic Network for Smart Grids/MEAN4SG This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 676042-MEAN4SG info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.702 2019 ENERGY & FUELS 63 / 112 = 0.562 2019 Q3 T2 0.635 2019 Control and Optimization 2019 Q2 Electrical and Electronic Engineering 2019 Q2 Renewable Energy, Sustainability and the Environment 2019 Q2 Energy Engineering and Power Technology 2019 Q2 Fuel Technology 2019 Q2 Energy (miscellaneous) 2019 Q2 info:eu-repo/semantics/review info:eu-repo/semantics/publishedVersion (orcid)0000-0003-3431-5863 Buldain, David Universidad de Zaragoza (orcid)0000-0002-4133-7553 Comech, María Paz Universidad de Zaragoza Gilbert, Ian Orue, Orue 5008 785 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Tecnología Electrónica 5009 535 Universidad de Zaragoza Dpto. Ingeniería Eléctrica Área Ingeniería Eléctrica 12, 13 (2019), 2485 [16 pp.] ENERGIES Energies 1996-1073 3533701 http://zaguan.unizar.es/record/86188/files/texto_completo.pdf Versión publicada 109110 http://zaguan.unizar.es/record/86188/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:86188 articulos driver 2020-07-16-09:57:18 ARTICLE