基于粗糙集和RBF神经网络的变压器故障诊断方法研究
发布时间：2018-07-24 08:48
【摘要】：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。
[Abstract]:Aiming at the problems of complex network structure and long training time in transformer fault diagnosis neural network model, a transformer fault diagnosis method based on rough set and RBF neural network is proposed. In this paper, a knowledge reduction algorithm based on discernible matrix and information entropy is established by using the no-decision analysis in rough set theory, data mining is carried out to find the minimum reduction, and the processed data set is used as the training sample. The Gao Si function is used as the radial basis function to solve the variance and the weights of each layer, and the transformer fault diagnosis model is established. The test results show that this algorithm has the advantages of simple network structure, fast training speed and strong generalization ability, although it slightly reduces the correct rate of diagnosis. It has a good guiding significance for improving the application performance of neural network in transformer fault diagnosis.
【作者单位】： 南京工程学院电力工程学院;江苏省高校"配电网智能技术与装备"协同创新中心;国网江苏省电力公司;国网南通供电公司;
【基金】：江苏省高校自然科学研究基金面上项目(13KJB470006) 江苏省电力公司2014年科技项目(J2014090) 江苏省电力公司2015年科技项目
【分类号】：TP183;TM407
[Abstract]:Aiming at the problems of complex network structure and long training time in transformer fault diagnosis neural network model, a transformer fault diagnosis method based on rough set and RBF neural network is proposed. In this paper, a knowledge reduction algorithm based on discernible matrix and information entropy is established by using the no-decision analysis in rough set theory, data mining is carried out to find the minimum reduction, and the processed data set is used as the training sample. The Gao Si function is used as the radial basis function to solve the variance and the weights of each layer, and the transformer fault diagnosis model is established. The test results show that this algorithm has the advantages of simple network structure, fast training speed and strong generalization ability, although it slightly reduces the correct rate of diagnosis. It has a good guiding significance for improving the application performance of neural network in transformer fault diagnosis.
【作者单位】： 南京工程学院电力工程学院;江苏省高校"配电网智能技术与装备"协同创新中心;国网江苏省电力公司;国网南通供电公司;
【基金】：江苏省高校自然科学研究基金面上项目(13KJB470006) 江苏省电力公司2014年科技项目(J2014090) 江苏省电力公司2015年科技项目
【分类号】：TP183;TM407
【参考文献】
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