108362 20211115162711.0 doi 10.1109/TPWRD.2020.3042934 sideral 125012 ART-2020-125012 eng Barrios, Sonia Partial Discharge Identification in MV switchgear using Scalogram representations and Convolutional AutoEncoder 2020 This work proposes a methodology to automate the recognition of Partial Discharges (PD) sources in Electrical Distribution Networks using a Deep Neural Network (DNN) model called Convolutional Autoencoder (CAE), which is able to automatically extract features from data to classify different sources. The database used to train the model is constructed with real defects commonly found in MV switchgear in service, and it also includes noise and interference signals that are present in these installations. PD sources consist of defective mountings, such as the loss of sealing cap of cable terminations, or an earth cable in contact with cable termination insulation. Four sources were replicated in a Smart Grid Laboratory and on-line measurement techniques were used to obtain the PD signal data. The Continuous Wavelet Transform (CWT) was applied to post-process the PD signal into a time-frequency image representation. The trained model predicts with high accuracy new data, demonstrating the effectiveness of the methodology to automate the recognition of different partial discharges and to differentiate them from noise and other interference sources. Access copy available to the general public Unrestricted info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 4.131 2020 ENGINEERING, ELECTRICAL & ELECTRONIC 58 / 273 = 0.212 2020 Q1 T1 1.57 2020 Energy Engineering and Power Technology 2020 Q1 Electrical and Electronic Engineering 2020 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Buldain, David Universidad de Zaragoza (orcid)0000-0003-3431-5863 Comech, Maria Paz Universidad de Zaragoza (orcid)0000-0002-4133-7553 Gilbert, Ian 5008 785 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Tecnología Electrónica 5009 535 Universidad de Zaragoza Dpto. Ingeniería Eléctrica Área Ingeniería Eléctrica (2020), [8 pp.] IEEE trans. power deliv. IEEE TRANSACTIONS ON POWER DELIVERY 0885-8977 1521719 http://zaguan.unizar.es/record/108362/files/texto_completo.pdf Postprint 3062242 http://zaguan.unizar.es/record/108362/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:108362 articulos driver 2021-11-15-12:18:41 ARTICLE