基于小波去噪与改进RBF神经网络的小电流接地系统故障选线方法  

A fault line selection method of small current grounding system based on wavelet de-noising and improved RBF neural network

作　　者：王晓卫[1] 魏向向[1] 侯雅晓 高杰[1] 

机构地区：[1]河南理工大学电气工程与自动化学院,河南焦作454003

出　　处：《工矿自动化》2014年第4期46-50,共5页Journal Of Mine Automation

基　　金：河南省教育厅科学技术研究重点项目(12B470002);河南省控制工程重点学科开放实验室资助项目(KG2011-15);河南理工大学青年基金(Q2012-28)

摘　　要：提出了一种基于小波去噪与改进RBF神经网络的小电流接地系统故障选线方法。将消噪后的零序电流绝对值的最大值进行归一化处理后得到故障信息矩阵,并将该矩阵作为RBF神经网络的输入;计算RBF神经网络输入层的活跃值,当活跃值在设定范围内时,RBF神经网络的隐含层与输出层自动断开,隐含层神经元分裂,待网络中权值、方差、中心值等参数自动调整后,RBF神经网络的隐含层与输出层重新连接,输出训练结果;将测试集输入到训练好的RBF神经网络,得出故障选线结果。算例分析结果表明,该选线方法不受故障相位角、接地电阻的影响,故障选线准确、可靠。The paper proposed a fault line selection method of small current grounding system based on wavelet de-noising and improved RBF neural network. Fault information matrix is obtained after normalization processing for maximum of absolute value of de-noised zero sequence current, and the matrix is used as input of RBF neural network. Active value of input layer of the RBF neural network is calculated, and when the active value is in preset range, hidden layer and output layer of the RBF neural network are disconnected automatically, and neurons of the hidden layer split. After parameters such as weight, variance and central value of the RBF neural network are adjusted, the hidden layer and the output layer are reconnected and training result is output. Test sets are input into the trained RBF neural network to get fault line selection result. The example analysis result shows that the method cannot be influenced by fault phase angle and grounding resistance with accurate and reliable fault line selection.

关 键 词：小电流接地系统 故障选线 小波去噪 RBF神经网络 活跃值 

分 类 号：TD608[矿业工程—矿山机电]