  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 S\'etif 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.....
