关键词: 车载牵引变压器, 短路故障, 小波神经网络, 混合粒子群算法

Abstract: The traction transformer faults diagnosis method is the combination of artificial intelligence algorithm and Dissolved Gas Analysis（DGA）. However, due to the reasons of regeneration, sampling and chromatographic analysis, the data from DGA exist many uncertainties. In view of these, a new method combining the electric parameters and a new wavelet neural network model is proposed to diagnosis the traction transformer faults. The electric parameters work as input signal of the new network model. The hidden layer uses orthogonal Daubechies function as basis function. Learning and optimization algorithm adopts a kind of hybrid particle swarm optimization algorithm that introduces the concepts of quantum computation and immune algorithm. The test results show that the proposed intelligent faults diagnosis algorithm owns the faster diagnosis speed and higher accuracy.

Key words: on-board traction transformer, short-circuit fault, wavelet neural network, hybrid particle swarm optimization