Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting

Contaminated insulators can have higher surface conductivity, which can result in irreversible failures in the electrical power system. In this paper, the ultrasound equipment is used to assist in the prediction of failure identification in porcelain insulators of the 13.8 kV, 60 Hz pin profile. To...

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Autores principales:	Stefano Frizzo Stefenon, Matheus Henrique Dal Molin Ribeiro, Ademir Nied, Viviana Cocco Mariani, Leandro Dos Santos Coelho, Valderi Reis Quietinho Leithardt, Luis Augusto Silva, Laio Oriel Seman
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Electric power system ensemble learning model grid inspection wavelet packet transform Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/067eb14237004a05a965a4711ec70da5
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id	oai:doaj.org-article:067eb14237004a05a965a4711ec70da5
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spelling	oai:doaj.org-article:067eb14237004a05a965a4711ec70da52021-11-23T00:01:04ZHybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting2169-353610.1109/ACCESS.2021.3076410https://doaj.org/article/067eb14237004a05a965a4711ec70da52021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9417227/https://doaj.org/toc/2169-3536Contaminated insulators can have higher surface conductivity, which can result in irreversible failures in the electrical power system. In this paper, the ultrasound equipment is used to assist in the prediction of failure identification in porcelain insulators of the 13.8 kV, 60 Hz pin profile. To perform the laboratory analysis, insulators from a problematic branch are removed after an inspection of the electrical system and are evaluated in the laboratory under controlled conditions. To perform the time series predictions, the stacking ensemble learning model is applied with the wavelet transform for signal filtering and noise reduction. For a complete analysis of the model, variations in its configuration were evaluated. The results of root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R<sup>2</sup>) are presented. To validate the result, a benchmarking is presented with well-established models, such as an adaptive neuro-fuzzy inference system (ANFIS) and long-term short-term memory (LSTM).Stefano Frizzo StefenonMatheus Henrique Dal Molin RibeiroAdemir NiedViviana Cocco MarianiLeandro Dos Santos CoelhoValderi Reis Quietinho LeithardtLuis Augusto SilvaLaio Oriel SemanIEEEarticleElectric power systemensemble learning modelgrid inspectionwavelet packet transformElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 66387-66397 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electric power system ensemble learning model grid inspection wavelet packet transform Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electric power system ensemble learning model grid inspection wavelet packet transform Electrical engineering. Electronics. Nuclear engineering TK1-9971 Stefano Frizzo Stefenon Matheus Henrique Dal Molin Ribeiro Ademir Nied Viviana Cocco Mariani Leandro Dos Santos Coelho Valderi Reis Quietinho Leithardt Luis Augusto Silva Laio Oriel Seman Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting
description	Contaminated insulators can have higher surface conductivity, which can result in irreversible failures in the electrical power system. In this paper, the ultrasound equipment is used to assist in the prediction of failure identification in porcelain insulators of the 13.8 kV, 60 Hz pin profile. To perform the laboratory analysis, insulators from a problematic branch are removed after an inspection of the electrical system and are evaluated in the laboratory under controlled conditions. To perform the time series predictions, the stacking ensemble learning model is applied with the wavelet transform for signal filtering and noise reduction. For a complete analysis of the model, variations in its configuration were evaluated. The results of root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R<sup>2</sup>) are presented. To validate the result, a benchmarking is presented with well-established models, such as an adaptive neuro-fuzzy inference system (ANFIS) and long-term short-term memory (LSTM).
format	article
author	Stefano Frizzo Stefenon Matheus Henrique Dal Molin Ribeiro Ademir Nied Viviana Cocco Mariani Leandro Dos Santos Coelho Valderi Reis Quietinho Leithardt Luis Augusto Silva Laio Oriel Seman
author_facet	Stefano Frizzo Stefenon Matheus Henrique Dal Molin Ribeiro Ademir Nied Viviana Cocco Mariani Leandro Dos Santos Coelho Valderi Reis Quietinho Leithardt Luis Augusto Silva Laio Oriel Seman
author_sort	Stefano Frizzo Stefenon
title	Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting
title_short	Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting
title_full	Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting
title_fullStr	Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting
title_full_unstemmed	Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting
title_sort	hybrid wavelet stacking ensemble model for insulators contamination forecasting
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/067eb14237004a05a965a4711ec70da5
work_keys_str_mv	AT stefanofrizzostefenon hybridwaveletstackingensemblemodelforinsulatorscontaminationforecasting AT matheushenriquedalmolinribeiro hybridwaveletstackingensemblemodelforinsulatorscontaminationforecasting AT ademirnied hybridwaveletstackingensemblemodelforinsulatorscontaminationforecasting AT vivianacoccomariani hybridwaveletstackingensemblemodelforinsulatorscontaminationforecasting AT leandrodossantoscoelho hybridwaveletstackingensemblemodelforinsulatorscontaminationforecasting AT valderireisquietinholeithardt hybridwaveletstackingensemblemodelforinsulatorscontaminationforecasting AT luisaugustosilva hybridwaveletstackingensemblemodelforinsulatorscontaminationforecasting AT laioorielseman hybridwaveletstackingensemblemodelforinsulatorscontaminationforecasting
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Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting

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