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Nowadays, power transformer aging and failures are viewed with greatattention in power transmission industry. Dissolved gas analysis (DGA) isclassified among the biggest widely used methods used within the context ofasset management policy to detect the incipient faults in their earlier stagein power transformers. Up to now, several procedures have been employed for thelecture of DGA results. Among these useful means, we find Key Gases, RogersRatios, IEC Ratios, the historical technique less used today DoernenburgRatios, the two types of Duval Pentagons methods, several versions of the DuvalTriangles method and Logarithmic Nomograph. Problem. DGA data extracted fromdifferent units in service served to verify the ability and reliability ofthese methods in assessing the state of health of the power transformer. Aim.An improving the quality of diagnostics of electrical power transformer byartificial neural network tools based on two conventional methods in the caseof a functional power transformer at S\'etif province in East North of Algeria.Methodology. Design an inelegant tool for power transformer diagnosis usingneural networks based on traditional methods IEC and Rogers, which allows toearly detection faults, to increase the reliability, of the entire electricalenergy system from transport to consumers and improve a continuity and qualityof service. Results. The solution of the problem was carried out by usingfeed-forward back-propagation neural networks implemented in MATLAB-Simulinkenvironment. Four real power transformers working under different environmentand climate conditions such as: desert, humid, cold were taken into account.The practical results of the diagnosis of these power transformers by the DGAare presented. Practical value

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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