Application of BAM Network in Fault Diagnosis of Oil-Immerseed Transformer

Bidirectional Associative Memory (BAM) network is presented to analysis the fault of power transformer. In order to improve the classification accuracy, the conception of combination is introduced. The fault diagnosis of power transformer is consisted of 4 BAM networks. The first BAM network is used to classify the normal and fault. The second BAM network is used to classify the heat fault and partial discharge (PD) fault. The third BAM network is used to classify MC-overheating faults in magnetic circuit and EC-overheating faults in electrical circuit. The fourth BAM network is used to classify RSI-discharge faults related to solid insulation, USI-discharge faults unrelated to solid insulation. By comparing with the RBF neural network algorithm for the same 90 input set, we conclude that the BAM network a good classifier for the fault diagnosis of power transformer.