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

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...

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Date:2017
Main Authors: Bondarenko, V. E., Shutenko, O. V.
Format: Article
Language:English
Ukrainian
Published: National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine 2017
Subjects:
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
621.314
Online Access:http://eie.khpi.edu.ua/article/view/2074-272X.2017.2.08
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Journal Title:Electrical Engineering & Electromechanics

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Electrical Engineering & Electromechanics
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spelling eiekhpieduua-article-1006792017-06-23T12:11:26Z DEVELOPMENT OF FUZZY NEURAL NETWORK FOR THE INTERPRETATION OF THE RESULTS OF DISSOLVED IN OIL GASES ANALYSIS РАЗРАБОТКА НЕЧЕТКОЙ НЕЙРОННОЙ СЕТИ ДЛЯ ИНТЕРПРЕТАЦИИ РЕЗУЛЬТАТОВ АНАЛИЗА РАСТВОРЕННЫХ В МАСЛЕ ГАЗОВ Bondarenko, V. E. Shutenko, O. V. 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 621.314 диагностика трансформаторов анализ растворенных в масле газов особенности газосодержания уровни концентраций нечеткие нейронные сети функции принадлежности распределение Вейбулла обучение сети нечеткий вывод ошибочные решения 621.314 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. Разработана и обучена нечеткая нейронная сеть для интерпретации результатов хроматографического анализа растворенных в масле газов. Предложено определять функции принадлежности лингвистических термов с учетом функций плотностей распределения концентраций газов для трансформаторов с различным состоянием. Выполнено тестирование обученной сети на независимой выборке. Проанализированы возможности нейронных сетей распознавать развивающиеся дефекты на ранней стадии их развития, или рост концентраций газов в исправных трансформаторах, после аварийных воздействий со стороны электрических сетей. National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine 2017-04-29 Article Article application/pdf application/pdf http://eie.khpi.edu.ua/article/view/2074-272X.2017.2.08 10.20998/2074-272X.2017.2.08 Electrical Engineering & Electromechanics; No. 2 (2017); 49-56 Электротехника и Электромеханика; № 2 (2017); 49-56 Електротехніка і Електромеханіка; № 2 (2017); 49-56 2309-3404 2074-272X en uk http://eie.khpi.edu.ua/article/view/2074-272X.2017.2.08/95896 http://eie.khpi.edu.ua/article/view/2074-272X.2017.2.08/95897 Copyright (c) 2017 V. E. Bondarenko, O. V. Shutenko https://creativecommons.org/licenses/by-nc/4.0
institution Electrical Engineering & Electromechanics
baseUrl_str
datestamp_date 2017-06-23T12:11:26Z
collection OJS
language English
Ukrainian
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
621.314
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
621.314
Bondarenko, V. E.
Shutenko, O. V.
DEVELOPMENT OF FUZZY NEURAL NETWORK FOR THE INTERPRETATION OF THE RESULTS OF DISSOLVED IN OIL GASES ANALYSIS
topic_facet 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
621.314
диагностика трансформаторов
анализ растворенных в масле газов
особенности газосодержания
уровни концентраций
нечеткие нейронные сети
функции принадлежности
распределение Вейбулла
обучение сети
нечеткий вывод
ошибочные решения
621.314
format Article
author Bondarenko, V. E.
Shutenko, O. V.
author_facet Bondarenko, V. E.
Shutenko, O. V.
author_sort Bondarenko, V. E.
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
title_alt РАЗРАБОТКА НЕЧЕТКОЙ НЕЙРОННОЙ СЕТИ ДЛЯ ИНТЕРПРЕТАЦИИ РЕЗУЛЬТАТОВ АНАЛИЗА РАСТВОРЕННЫХ В МАСЛЕ ГАЗОВ
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.
publisher National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine
publishDate 2017
url http://eie.khpi.edu.ua/article/view/2074-272X.2017.2.08
work_keys_str_mv AT bondarenkove developmentoffuzzyneuralnetworkfortheinterpretationoftheresultsofdissolvedinoilgasesanalysis
AT shutenkoov developmentoffuzzyneuralnetworkfortheinterpretationoftheresultsofdissolvedinoilgasesanalysis
AT bondarenkove razrabotkanečetkojnejronnojsetidlâinterpretaciirezulʹtatovanalizarastvorennyhvmaslegazov
AT shutenkoov razrabotkanečetkojnejronnojsetidlâinterpretaciirezulʹtatovanalizarastvorennyhvmaslegazov
first_indexed 2025-07-17T11:45:52Z
last_indexed 2025-07-17T11:45:52Z
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