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Inception 1D-convolutional neural network for accurate prediction of electrical insulator leakage current from environmental data during its normal operation using long-term recording

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Inception 1D-convolutional neural network for accurate prediction of electrical insulator leakage current from environmental data during its normal operation using long-term recording

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Bueno Barrachina, JM.; Ye Lin, Y.; Nieto Del-Amor, F.; Fuster Roig, VL. (2023). Inception 1D-convolutional neural network for accurate prediction of electrical insulator leakage current from environmental data during its normal operation using long-term recording. Engineering Applications of Artificial Intelligence. 119. https://doi.org/10.1016/j.engappai.2022.105799

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/203380

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Title: Inception 1D-convolutional neural network for accurate prediction of electrical insulator leakage current from environmental data during its normal operation using long-term recording
Author: Bueno Barrachina, José Manuel Ye Lin, Yiyao Nieto del-Amor, Félix Fuster Roig, Vicente Luis
UPV Unit: Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials
Issued date:
Abstract:
[EN] Contamination flashover remains one of the biggest challenges for power grid designers and maintenance engineers. Insulator leakage current contains relevant information about their state so that continuous monitoring ...[+]
Subjects: Convolutional neural network , Insulator leakage current prediction , Inception architecture , Conditional Granger causality , Contamination flashover , Support Vector Regression
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Engineering Applications of Artificial Intelligence. (issn: 0952-1976 )
DOI: 10.1016/j.engappai.2022.105799
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.engappai.2022.105799
Project ID:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094449-A-I00/ES/ELECTROHISTEROGRAFIA PARA LA MEJORA EN LA TOMA DE DECISIONES EN SITUACIONES DE RIESGO EN OBSTETRICIA: PARTO PREMATURO E INDUCCION DEL PARTO/
Thanks:
This work was supported by the Spanish Ministry of Economy and Competitiveness, Spain, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR) .
Type: Artículo

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