Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems

Introduction. One of the parameters that determine the state of the insulation of power transformers is the degree of moisture content of cellulose insulation and transformer oil. Modern systems of continuous monitoring of transformer equipment have the ability to accumulate data that can be used to...

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Autores principales:	V.V. Vasilevskij, M.O. Poliakov
Formato:	article
Lenguaje:	EN RU UK
Publicado:	National Technical University "Kharkiv Polytechnic Institute" 2021
Materias:	power transformer transformer oil cellulose insulation anfis modeling Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/9bb977c544eb4220b360b17e93a7561f
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spelling	oai:doaj.org-article:9bb977c544eb4220b360b17e93a7561f2021-12-02T16:27:39ZReproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems10.20998/2074-272X.2021.1.022074-272X2309-3404https://doaj.org/article/9bb977c544eb4220b360b17e93a7561f2021-02-01T00:00:00Zhttp://eie.khpi.edu.ua/article/view/225152/225150https://doaj.org/toc/2074-272Xhttps://doaj.org/toc/2309-3404Introduction. One of the parameters that determine the state of the insulation of power transformers is the degree of moisture content of cellulose insulation and transformer oil. Modern systems of continuous monitoring of transformer equipment have the ability to accumulate data that can be used to reproduce the dynamics of moisture content in insulation. The purpose of the work is to reproduce the curve of the of humidity of transformer oil based on the results of measuring the temperature of the upper and lower layers of oil without the need for direct measurement of moisture content by special devices. Methodology. The construction of a fuzzy neural network is carried out using networks based on adaptive neuro-fuzzy system ANFIS. The network generated using the Grid Partition algorithm without clustering and Subtractive Clustering. Results. The paper presents a comparative analysis of fuzzy neural networks of various architectures in terms of increasing the accuracy of reproducing the moisture content of transformer oil. For training and testing fuzzy neural networks, the results of continuous monitoring of the temperature of the upper and lower layers of transformer oil during two months of operation used. Considered twenty four variants of the architecture of ANFIS models, which differ in the membership functions, the number of terms of each input quantity, and the number of training cycles. The results of using the constructed fuzzy neural networks for reproducing the dynamics of moisture content of transformer oil during a month of operation of the transformer are presented. The reproducing accuracy was assessed using the root mean square error and the coefficient of determination. The test results indicate the sufficient adequacy of the proposed models. Consequently, the RMSE value for the network constructed using Grid Partition method was 0.49, and for the network built using the Subtractive Clustering method – 0.40509.V.V. VasilevskijM.O. PoliakovNational Technical University "Kharkiv Polytechnic Institute"articlepower transformertransformer oilcellulose insulationanfismodelingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENRUUKElectrical engineering & Electromechanics, Iss 1, Pp 10-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN RU UK
topic	power transformer transformer oil cellulose insulation anfis modeling Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	power transformer transformer oil cellulose insulation anfis modeling Electrical engineering. Electronics. Nuclear engineering TK1-9971 V.V. Vasilevskij M.O. Poliakov Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems
description	Introduction. One of the parameters that determine the state of the insulation of power transformers is the degree of moisture content of cellulose insulation and transformer oil. Modern systems of continuous monitoring of transformer equipment have the ability to accumulate data that can be used to reproduce the dynamics of moisture content in insulation. The purpose of the work is to reproduce the curve of the of humidity of transformer oil based on the results of measuring the temperature of the upper and lower layers of oil without the need for direct measurement of moisture content by special devices. Methodology. The construction of a fuzzy neural network is carried out using networks based on adaptive neuro-fuzzy system ANFIS. The network generated using the Grid Partition algorithm without clustering and Subtractive Clustering. Results. The paper presents a comparative analysis of fuzzy neural networks of various architectures in terms of increasing the accuracy of reproducing the moisture content of transformer oil. For training and testing fuzzy neural networks, the results of continuous monitoring of the temperature of the upper and lower layers of transformer oil during two months of operation used. Considered twenty four variants of the architecture of ANFIS models, which differ in the membership functions, the number of terms of each input quantity, and the number of training cycles. The results of using the constructed fuzzy neural networks for reproducing the dynamics of moisture content of transformer oil during a month of operation of the transformer are presented. The reproducing accuracy was assessed using the root mean square error and the coefficient of determination. The test results indicate the sufficient adequacy of the proposed models. Consequently, the RMSE value for the network constructed using Grid Partition method was 0.49, and for the network built using the Subtractive Clustering method – 0.40509.
format	article
author	V.V. Vasilevskij M.O. Poliakov
author_facet	V.V. Vasilevskij M.O. Poliakov
author_sort	V.V. Vasilevskij
title	Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems
title_short	Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems
title_full	Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems
title_fullStr	Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems
title_full_unstemmed	Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems
title_sort	reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems
publisher	National Technical University "Kharkiv Polytechnic Institute"
publishDate	2021
url	https://doaj.org/article/9bb977c544eb4220b360b17e93a7561f
work_keys_str_mv	AT vvvasilevskij reproducingofthehumiditycurveofpowertransformersoilusingadaptiveneurofuzzysystems AT mopoliakov reproducingofthehumiditycurveofpowertransformersoilusingadaptiveneurofuzzysystems
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Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems

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