Power transformer being a major apparatus in a power system, monitoring of its in-service behavior is necessary to avoid catastrophic failures, costly outages. Power transformers are considered capital investments in the infrastructure of every power system in the world. Dissolved Gas Analysis (DGA) is an effective method for the early detection of incipient fault in power transformers. But the main drawback of the DGA ratio methods is that they fail to cover all ranges of data. The interesting results revealed by this study are serving as the underlying principle to carry out an ANN based insulation condition monitoring system. ANN approach is automatically capable of handling highly nonlinear input output relationships, acquiring experiences which are unknown to human experts from training data and also to generalize solutions for a new set of data.
Artificial Neural Network (ANN), Dissolved Gas Analysis (DGA).
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