An Efficient Approach With Application of Linear and Nonlinear Models for Evaluation of Power Transformer Health Index

In this paper, efficient and accurate linear and nonlinear models are proposed for indicating comprehensive health requirements of the transformer using health index (HI) concept. The models are established with 336 experimental datasets including oil characteristics and dissolved gas analysis (DGA)...

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Autores principales:	Hamed Zeinoddini-Meymand, Salah Kamel, Baseem Khan
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	ANFIS ANN condition assessment health index lifetime management MLR Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/193eafc243124d39996136171b26d548
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spelling	oai:doaj.org-article:193eafc243124d39996136171b26d5482021-11-18T00:01:51ZAn Efficient Approach With Application of Linear and Nonlinear Models for Evaluation of Power Transformer Health Index2169-353610.1109/ACCESS.2021.3124845https://doaj.org/article/193eafc243124d39996136171b26d5482021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9598872/https://doaj.org/toc/2169-3536In this paper, efficient and accurate linear and nonlinear models are proposed for indicating comprehensive health requirements of the transformer using health index (HI) concept. The models are established with 336 experimental datasets including oil characteristics and dissolved gas analysis (DGA) of various types of transformers placed in different areas. The significance of DGA parameters in transformer health condition is considered with the inclusive DGA factor (<italic>DGAF</italic>) parameter, which considers the weighting importance of seven dissolved gases. Nonlinear models used in this paper are artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), which represent the behavior of transformer insulation parameters. The nonlinear models are compared with multiple linear regression (MLR) which is a linear statistical model. The models are established with 80 percent of the experimental dataset. The other 20 percent of data are utilized for the efficiency assessment of the models. The results demonstrate that the models provide an assessment of the health condition of the transformers comparable to existing models with high accuracy. The contributions of this paper are: 1) Evaluating the overall HI of the transformer employing a complete set of 15 input parameters of transformer oil-paper insulation system. 2) Adding <italic>DGAF</italic>, <italic>%WaterPaper</italic>, <italic>IFT</italic> parameters and showing the importance of these parameters. 3) Regarding the condition of solid insulation of the transformer particularly. 4) Applying a diverse and large practical dataset composed of 336 different transformers located in different country areas. 5) Using the MLR method for three purposes. 6) Providing linear (MLR) and nonlinear (ANN, ANFIS) models for HI calculation of the dataset, simultaneously. 7) Verifying the applicability and efficiency of the ANFIS model for simulating HI value.Hamed Zeinoddini-MeymandSalah KamelBaseem KhanIEEEarticleANFISANNcondition assessmenthealth indexlifetime managementMLRElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150172-150186 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ANFIS ANN condition assessment health index lifetime management MLR Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	ANFIS ANN condition assessment health index lifetime management MLR Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hamed Zeinoddini-Meymand Salah Kamel Baseem Khan An Efficient Approach With Application of Linear and Nonlinear Models for Evaluation of Power Transformer Health Index
description	In this paper, efficient and accurate linear and nonlinear models are proposed for indicating comprehensive health requirements of the transformer using health index (HI) concept. The models are established with 336 experimental datasets including oil characteristics and dissolved gas analysis (DGA) of various types of transformers placed in different areas. The significance of DGA parameters in transformer health condition is considered with the inclusive DGA factor (<italic>DGAF</italic>) parameter, which considers the weighting importance of seven dissolved gases. Nonlinear models used in this paper are artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), which represent the behavior of transformer insulation parameters. The nonlinear models are compared with multiple linear regression (MLR) which is a linear statistical model. The models are established with 80 percent of the experimental dataset. The other 20 percent of data are utilized for the efficiency assessment of the models. The results demonstrate that the models provide an assessment of the health condition of the transformers comparable to existing models with high accuracy. The contributions of this paper are: 1) Evaluating the overall HI of the transformer employing a complete set of 15 input parameters of transformer oil-paper insulation system. 2) Adding <italic>DGAF</italic>, <italic>%WaterPaper</italic>, <italic>IFT</italic> parameters and showing the importance of these parameters. 3) Regarding the condition of solid insulation of the transformer particularly. 4) Applying a diverse and large practical dataset composed of 336 different transformers located in different country areas. 5) Using the MLR method for three purposes. 6) Providing linear (MLR) and nonlinear (ANN, ANFIS) models for HI calculation of the dataset, simultaneously. 7) Verifying the applicability and efficiency of the ANFIS model for simulating HI value.
format	article
author	Hamed Zeinoddini-Meymand Salah Kamel Baseem Khan
author_facet	Hamed Zeinoddini-Meymand Salah Kamel Baseem Khan
author_sort	Hamed Zeinoddini-Meymand
title	An Efficient Approach With Application of Linear and Nonlinear Models for Evaluation of Power Transformer Health Index
title_short	An Efficient Approach With Application of Linear and Nonlinear Models for Evaluation of Power Transformer Health Index
title_full	An Efficient Approach With Application of Linear and Nonlinear Models for Evaluation of Power Transformer Health Index
title_fullStr	An Efficient Approach With Application of Linear and Nonlinear Models for Evaluation of Power Transformer Health Index
title_full_unstemmed	An Efficient Approach With Application of Linear and Nonlinear Models for Evaluation of Power Transformer Health Index
title_sort	efficient approach with application of linear and nonlinear models for evaluation of power transformer health index
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/193eafc243124d39996136171b26d548
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An Efficient Approach With Application of Linear and Nonlinear Models for Evaluation of Power Transformer Health Index

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