Dissolved Gas Analysis (DGA) is a popular method to detect and diagnose different types of faults occurring in power transformers. This objective is obtained by employing different interpretations of dissolved gases in the mineral oil insulation of such transformers. This paper engages these interpretations and applies appropriate Artificial Neural Networks (ANN) to classify the different faults. Each interpretation method needs special neural network to determine the occurred fault. Three ANNs are applied to this aim. The classification results and some typical examples are presented to validate the networks.
Key words: DGA, duval triangle, ANN, power transformer faults.
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