改进型组合RBF神经网络的变压器故障诊断

Application of Modified Combinatorial Radial Basis Function Neural Network in Fault Diagnosis of Power Transformer

提出了一种在逼近能力、分类能力、学习速度等方面都优于BP神经网络的径向基函数神经网络和组合诊断的概念,并将其应用到变压器DGA故障诊断中。在处理输入数据和改进训练方法后,组合RBF神经网络诊断变压器故障训练速度快、收敛精度高、诊断准确。

This paper proposes a RBF Neural Network and the conception of combinatorial diagnosis and applies them in fault diagnosis of power transformen The RBF Neural Network is prior to BP Neural Network in the ability of approach , the ability of classification and the rate of train. After the input data is processed and the training method is modified, combinatorial radial basis function neural network has a rapid train rate, high convergence precision and high diagnosis veracity in fault diagnosis of power transformer.