Application of Learning Vector Quantization network in fault diagnosis of power transformer

Abstract

Learning Vector Quantization (LVQ) network is presented to analysis the fault of power transformer. The oil gases extracted from transformer oil form the input vector of LVQ network. The connection weights vector is determined with teacher guide. Compared with radius function neural network (RBFNN), LVQ network is easy to perform with high efficiency. In order to improve the classification accuracy, the conception of combination is introduced. The fault diagnosis of power transformer is consisted of 4 LVQ networks. The first LVQ network is used to classify the normal and fault. The second LVQ network is used to classify the heat fault and partial discharge (PD) fault. The third LVQ network is used to classify MC-overheating faults in magnetic circuit and EC-overheating faults in electrical circuit. The fourth LVQ 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 122 input set, we conclude that the LVQ network a good classifier for the fault diagnosis of power transformer.