基于 SOM 神经网络的煤矿智能变电站故障录波启动判据算法 .pdfVIP

学兔兔 第46卷第8期 煤 炭 工 程 Vo1．46，No．8 2014年第8期 C0AL ENGINEERING No．8，2014 doi：i0．1 1799／ee2O1408043 基于SOM神经网络的煤矿智能变电站 故障录波启动判据算法 李天玉，盛芬芬，王一聪，褚 达，蔡儒军 (中国矿业大学信息与电气工程学院，江苏徐州 221116) 摘 要：由于传统故障录波启动判据算法具有一定局限性，论文提出一种基于SOM神经网 络的算法。以A相电流越限为例进行了算法的研究，依次完成SOM神经网络的构建，网络训练 以及聚类预测，将输入向量归一化后输入到训练好的SOM网络中，输出结果会在二维平面阵列 中显示出来，网络拓扑结构中的蓝色神经元代表A相越限，此时需要启动录波。为了验证模型 的正确性，依次将维数不同的两组向量输入网络模型中，输出结果表明，基于SOM神经网络的 故障录波启动判据算法自适应能力较强，能有效地完成录波启动，误差较小。 关键词：智能变电站；故障录波；启动判据；SOM神经网络；聚类分析 中图分类号：TD611 ．2 文献标识码：A 文章编号：1671—0959(2014)08-0136-04 Start Criteria Algorithm of Fault Recording for Coal Mine Smart Substation Based on SOM Neural Network LI Tian—yu，SHENG Fen—fen，WANG Yi—cong，CHU Da，CAI Ru—jun (School of Information and Electrical Engineering，China University of Mining Technology，Xuzhou 221116，China) Abstract：Aiming at the limit of criteria algorithm of traditional fault recording start，an algorithm based on SOM neural network was proposed．A fault of A—phase current out of limit was taken as example to study the algorithm．The SOM neural network was established and trained and cluster prediction was completed，then the normalized input vector was inputted to the trained SOM network，and the output was to be shown in dimensional plane array，and blue neurons in network topology represent phase A over limit，at this point，wave recording should be started．In order to verify the accuracy of the model，two input vector of different dimensionality were inputted into the netwo