基于粗糙集和RBF神经网络的变压器故障诊断方法研究

针对变压器故障诊断神经网络模型存在网络结构复杂、训练时间长等问题,提出基于粗糙集及RBF神经网络的变压器故障诊断方法。运用粗糙集理论中无决策分析,建立基于可分辨矩阵和信息熵的知识约简算法,进行数据挖掘,寻找最小约简;以处理后的数据集合作为训练样本,采用高斯函数作为径向基函数,分别求解方差及各层权值,建立变压器故障诊断模型。通过测试对比,此算法虽然略微降低诊断正确率,但网络结构简单、训练速度快、泛化能力强,对提高神经网络在变压器故障诊断中的应用性能有较好的指导意义。

Research of Transformer Fault Diagnosis Based on Rough Sets and RBF Neural Network

For the problems of complex network structure, long training time and other issues in classical neural networks, a method of RBF neural network combined with rough sets is proposed in this paper for transformers fault diagnosis. Firstly, the minimum reduction is calculated after data mining based on distinguishes matrix and information entropy in rough set theory. Then, using a Gaussian function as the radial basis function, the variance and weights of layers is calculated by the processed data collection as training samples. Finally, the transformer fault diagnosis model is built. By comparing the test results, although the algorithm in terms of diagnostic accuracy is slightly lower, but the simple network structure, training speed and strong generalization ability of neural networks to improve application performance in transformer fault diagnosis have better guidance.