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
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 ...[+]
[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 is considered the most effective way to prevent contamination flashover. In this work, we attempted to accurately predict insulator leakage current in real time during normal operations based on environmental data using long-term recordings.
We first confirmed that the history of environmental data also contained relevant information to predict leakage current by conditional Granger analysis and determined that 20 was the optimal previous samples number for this purpose. We then compared the performance of typical regression models and convolutional neural network (CNN), when using both current and the last 21 samples as input features. We confirmed that the model with the last 21 samples might perform significantly better. Input features pre-processing by cascaded inception architecture was fundamental to capture the complex dynamic interaction between environmental data and leakage current and significantly improved the model performance. CNN based on inception architecture performed much better, achieving an average R2 of 0.94 ±0.03. The proposed model could be used to predict leakage current in both porcelain insulators with or without coatings and silicone composite insulators.
Our results pave the way for creating an on-line pre-warning system adapted to individual installations, can anticipate the negative consequences of weather and/or pollution deposits and is useful for designing a strategic high-voltage electrical insulator preventive maintenance plan for preventing contamination flashover and thus increase power grid reliability and resilience.
[-]
|
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
|