000108362 001__ 108362
000108362 005__ 20211115162711.0
000108362 0247_ $$2doi$$a10.1109/TPWRD.2020.3042934
000108362 0248_ $$2sideral$$a125012
000108362 037__ $$aART-2020-125012
000108362 041__ $$aeng
000108362 100__ $$aBarrios, Sonia
000108362 245__ $$aPartial Discharge Identification in MV switchgear using Scalogram representations and Convolutional AutoEncoder
000108362 260__ $$c2020
000108362 5060_ $$aAccess copy available to the general public$$fUnrestricted
000108362 5203_ $$aThis 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.
000108362 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000108362 590__ $$a4.131$$b2020
000108362 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b58 / 273 = 0.212$$c2020$$dQ1$$eT1
000108362 592__ $$a1.57$$b2020
000108362 593__ $$aEnergy Engineering and Power Technology$$c2020$$dQ1
000108362 593__ $$aElectrical and Electronic Engineering$$c2020$$dQ1
000108362 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000108362 700__ $$0(orcid)0000-0003-3431-5863$$aBuldain, David$$uUniversidad de Zaragoza
000108362 700__ $$0(orcid)0000-0002-4133-7553$$aComech, Maria Paz$$uUniversidad de Zaragoza
000108362 700__ $$aGilbert, Ian
000108362 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000108362 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000108362 773__ $$g(2020), [8 pp.]$$pIEEE trans. power deliv.$$tIEEE TRANSACTIONS ON POWER DELIVERY$$x0885-8977
000108362 8564_ $$s1521719$$uhttps://zaguan.unizar.es/record/108362/files/texto_completo.pdf$$yPostprint
000108362 8564_ $$s3062242$$uhttps://zaguan.unizar.es/record/108362/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000108362 909CO $$ooai:zaguan.unizar.es:108362$$particulos$$pdriver
000108362 951__ $$a2021-11-15-12:18:41
000108362 980__ $$aARTICLE