Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study

Introduction. Nowadays, power transformer aging and failures are viewed with great attention in power transmission industry. Dissolved gas analysis (DGA) is classified among the biggest widely used methods used within the context of asset management policy to detect the incipient faults in their ear...

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Autores principales:	L. Bouchaoui, K. E. Hemsas, H. Mellah, S. Benlahneche
Formato:	article
Lenguaje:	EN RU UK
Publicado:	National Technical University "Kharkiv Polytechnic Institute" 2021
Materias:	analysis of dissolved gases in oil diagnostics of power transformers feed-forward neural networks rogers method iec method Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a8e56e027e3647afac5b7678b09a2691
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spelling	oai:doaj.org-article:a8e56e027e3647afac5b7678b09a26912021-12-02T16:57:39ZPower transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study10.20998/2074-272X.2021.4.012074-272X2309-3404https://doaj.org/article/a8e56e027e3647afac5b7678b09a26912021-07-01T00:00:00Zhttp://eie.khpi.edu.ua/article/view/237786/236477https://doaj.org/toc/2074-272Xhttps://doaj.org/toc/2309-3404Introduction. Nowadays, power transformer aging and failures are viewed with great attention in power transmission industry. Dissolved gas analysis (DGA) is classified among the biggest widely used methods used within the context of asset management policy to detect the incipient faults in their earlier stage in power transformers. Up to now, several procedures have been employed for the lecture of DGA results. Among these useful means, we find Key Gases, Rogers Ratios, IEC Ratios, the historical technique less used today Doernenburg Ratios, the two types of Duval Pentagons methods, several versions of the Duval Triangles method and Logarithmic Nomograph. Problem. DGA data extracted from different units in service served to verify the ability and reliability of these methods in assessing the state of health of the power transformer. Aim. An improving the quality of diagnostics of electrical power transformer by artificial neural network tools based on two conventional methods in the case of a functional power transformer at Sétif province in East North of Algeria. Methodology. Design an inelegant tool for power transformer diagnosis using neural networks based on traditional methods IEC and Rogers, which allows to early detection faults, to increase the reliability, of the entire electrical energy system from transport to consumers and improve a continuity and quality of service. Results. The solution of the problem was carried out by using feed-forward back-propagation neural networks implemented in MATLAB-Simulink environment. Four real power transformers working under different environment and climate conditions such as: desert, humid, cold were taken into account. The practical results of the diagnosis of these power transformers by the DGA are presented. Practical value. The structure and specific features of power transformer winding insulation ageing and defect state diagnosis by the application of the artificial neural network (ANN) has been briefly given. MATLAB programs were then developed to automate the evaluation of each method. This paper presents another tool to review the results obtained by the delta X software widely used by the electricity company in Algeria.L. BouchaouiK. E. HemsasH. MellahS. BenlahnecheNational Technical University "Kharkiv Polytechnic Institute"articleanalysis of dissolved gases in oildiagnostics of power transformersfeed-forward neural networksrogers methodiec methodElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENRUUKElectrical engineering & Electromechanics, Iss 4, Pp 3-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN RU UK
topic	analysis of dissolved gases in oil diagnostics of power transformers feed-forward neural networks rogers method iec method Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	analysis of dissolved gases in oil diagnostics of power transformers feed-forward neural networks rogers method iec method Electrical engineering. Electronics. Nuclear engineering TK1-9971 L. Bouchaoui K. E. Hemsas H. Mellah S. Benlahneche Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study
description	Introduction. Nowadays, power transformer aging and failures are viewed with great attention in power transmission industry. Dissolved gas analysis (DGA) is classified among the biggest widely used methods used within the context of asset management policy to detect the incipient faults in their earlier stage in power transformers. Up to now, several procedures have been employed for the lecture of DGA results. Among these useful means, we find Key Gases, Rogers Ratios, IEC Ratios, the historical technique less used today Doernenburg Ratios, the two types of Duval Pentagons methods, several versions of the Duval Triangles method and Logarithmic Nomograph. Problem. DGA data extracted from different units in service served to verify the ability and reliability of these methods in assessing the state of health of the power transformer. Aim. An improving the quality of diagnostics of electrical power transformer by artificial neural network tools based on two conventional methods in the case of a functional power transformer at Sétif province in East North of Algeria. Methodology. Design an inelegant tool for power transformer diagnosis using neural networks based on traditional methods IEC and Rogers, which allows to early detection faults, to increase the reliability, of the entire electrical energy system from transport to consumers and improve a continuity and quality of service. Results. The solution of the problem was carried out by using feed-forward back-propagation neural networks implemented in MATLAB-Simulink environment. Four real power transformers working under different environment and climate conditions such as: desert, humid, cold were taken into account. The practical results of the diagnosis of these power transformers by the DGA are presented. Practical value. The structure and specific features of power transformer winding insulation ageing and defect state diagnosis by the application of the artificial neural network (ANN) has been briefly given. MATLAB programs were then developed to automate the evaluation of each method. This paper presents another tool to review the results obtained by the delta X software widely used by the electricity company in Algeria.
format	article
author	L. Bouchaoui K. E. Hemsas H. Mellah S. Benlahneche
author_facet	L. Bouchaoui K. E. Hemsas H. Mellah S. Benlahneche
author_sort	L. Bouchaoui
title	Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study
title_short	Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study
title_full	Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study
title_fullStr	Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study
title_full_unstemmed	Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study
title_sort	power transformer faults diagnosis using undestructive methods (roger and iec) and artificial neural network for dissolved gas analysis applied on the functional transformer in the algerian north-eastern: a comparative study
publisher	National Technical University "Kharkiv Polytechnic Institute"
publishDate	2021
url	https://doaj.org/article/a8e56e027e3647afac5b7678b09a2691
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Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study

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