DEVELOPMENT OF FUZZY NEURAL NETWORK FOR THE INTERPRETATION OF THE RESULTS OF DISSOLVED IN OIL GASES ANALYSIS

Purpose. The purpose of this paper is a diagnosis of power transformers on the basis of the results of the analysis of gases dissolved in oil. Methodology. To solve this problem a fuzzy neural network has been developed, tested and trained. Results. The analysis of neural network to recognize the po...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	V.Е. Bondarenko, O.V. Shutenko
Formato:	article
Lenguaje:	EN RU UK
Publicado:	National Technical University "Kharkiv Polytechnic Institute" 2017
Materias:	diagnostics of transformers analysis of dissolved gases in oil peculiarities of gas content concentration levels fuzzy neural networks membership function Weibull distribution network training fuzzy conclusion wrong decisions Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a8329ae499464d3b8268acdf5ee34fa8
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a8329ae499464d3b8268acdf5ee34fa8
record_format	dspace
spelling	oai:doaj.org-article:a8329ae499464d3b8268acdf5ee34fa82021-12-02T18:09:46ZDEVELOPMENT OF FUZZY NEURAL NETWORK FOR THE INTERPRETATION OF THE RESULTS OF DISSOLVED IN OIL GASES ANALYSIS10.20998/2074-272X.2017.2.082074-272X2309-3404https://doaj.org/article/a8329ae499464d3b8268acdf5ee34fa82017-04-01T00:00:00Zhttp://eie.khpi.edu.ua/article/view/2074-272X.2017.2.08/95896https://doaj.org/toc/2074-272Xhttps://doaj.org/toc/2309-3404Purpose. The purpose of this paper is a diagnosis of power transformers on the basis of the results of the analysis of gases dissolved in oil. Methodology. To solve this problem a fuzzy neural network has been developed, tested and trained. Results. The analysis of neural network to recognize the possibility of developing defects at an early stage of their development, or growth of gas concentrations in the healthy transformers, made after the emergency actions on the part of electric networks is made. It has been established greatest difficulty in making a diagnosis on the criterion of the boundary gas concentrations, are the results of DGA obtained for the healthy transformers in which the concentration of gases dissolved in oil exceed their limit values, as well as defective transformers at an early stage development defects. The analysis showed that the accuracy of recognition of fuzzy neural networks has its limitations, which are determined by the peculiarities of the DGA method, used diagnostic features and the selected decision rule. Originality. Unlike similar studies in the training of the neural network, the membership functions of linguistic terms were chosen taking into account the functions gas concentrations density distribution transformers with various diagnoses, allowing to consider a particular gas content of oils that are typical of a leaky transformer, and the operating conditions of the equipment. Practical value. Developed fuzzy neural network allows to perform diagnostics of power transformers on the basis of the result of the analysis of gases dissolved in oil, with a high level of reliability. V.Е. BondarenkoO.V. ShutenkoNational Technical University "Kharkiv Polytechnic Institute"articlediagnostics of transformersanalysis of dissolved gases in oilpeculiarities of gas contentconcentration levelsfuzzy neural networksmembership functionWeibull distributionnetwork trainingfuzzy conclusionwrong decisionsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENRUUKElectrical engineering & Electromechanics, Iss 2, Pp 49-56 (2017)
institution	DOAJ
collection	DOAJ
language	EN RU UK
topic	diagnostics of transformers analysis of dissolved gases in oil peculiarities of gas content concentration levels fuzzy neural networks membership function Weibull distribution network training fuzzy conclusion wrong decisions Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	diagnostics of transformers analysis of dissolved gases in oil peculiarities of gas content concentration levels fuzzy neural networks membership function Weibull distribution network training fuzzy conclusion wrong decisions Electrical engineering. Electronics. Nuclear engineering TK1-9971 V.Е. Bondarenko O.V. Shutenko DEVELOPMENT OF FUZZY NEURAL NETWORK FOR THE INTERPRETATION OF THE RESULTS OF DISSOLVED IN OIL GASES ANALYSIS
description	Purpose. The purpose of this paper is a diagnosis of power transformers on the basis of the results of the analysis of gases dissolved in oil. Methodology. To solve this problem a fuzzy neural network has been developed, tested and trained. Results. The analysis of neural network to recognize the possibility of developing defects at an early stage of their development, or growth of gas concentrations in the healthy transformers, made after the emergency actions on the part of electric networks is made. It has been established greatest difficulty in making a diagnosis on the criterion of the boundary gas concentrations, are the results of DGA obtained for the healthy transformers in which the concentration of gases dissolved in oil exceed their limit values, as well as defective transformers at an early stage development defects. The analysis showed that the accuracy of recognition of fuzzy neural networks has its limitations, which are determined by the peculiarities of the DGA method, used diagnostic features and the selected decision rule. Originality. Unlike similar studies in the training of the neural network, the membership functions of linguistic terms were chosen taking into account the functions gas concentrations density distribution transformers with various diagnoses, allowing to consider a particular gas content of oils that are typical of a leaky transformer, and the operating conditions of the equipment. Practical value. Developed fuzzy neural network allows to perform diagnostics of power transformers on the basis of the result of the analysis of gases dissolved in oil, with a high level of reliability.
format	article
author	V.Е. Bondarenko O.V. Shutenko
author_facet	V.Е. Bondarenko O.V. Shutenko
author_sort	V.Е. Bondarenko
title	DEVELOPMENT OF FUZZY NEURAL NETWORK FOR THE INTERPRETATION OF THE RESULTS OF DISSOLVED IN OIL GASES ANALYSIS
title_short	DEVELOPMENT OF FUZZY NEURAL NETWORK FOR THE INTERPRETATION OF THE RESULTS OF DISSOLVED IN OIL GASES ANALYSIS
title_full	DEVELOPMENT OF FUZZY NEURAL NETWORK FOR THE INTERPRETATION OF THE RESULTS OF DISSOLVED IN OIL GASES ANALYSIS
title_fullStr	DEVELOPMENT OF FUZZY NEURAL NETWORK FOR THE INTERPRETATION OF THE RESULTS OF DISSOLVED IN OIL GASES ANALYSIS
title_full_unstemmed	DEVELOPMENT OF FUZZY NEURAL NETWORK FOR THE INTERPRETATION OF THE RESULTS OF DISSOLVED IN OIL GASES ANALYSIS
title_sort	development of fuzzy neural network for the interpretation of the results of dissolved in oil gases analysis
publisher	National Technical University "Kharkiv Polytechnic Institute"
publishDate	2017
url	https://doaj.org/article/a8329ae499464d3b8268acdf5ee34fa8
work_keys_str_mv	AT vebondarenko developmentoffuzzyneuralnetworkfortheinterpretationoftheresultsofdissolvedinoilgasesanalysis AT ovshutenko developmentoffuzzyneuralnetworkfortheinterpretationoftheresultsofdissolvedinoilgasesanalysis
_version_	1718378590781833216

DEVELOPMENT OF FUZZY NEURAL NETWORK FOR THE INTERPRETATION OF THE RESULTS OF DISSOLVED IN OIL GASES ANALYSIS

Ejemplares similares