煤炭工程 ›› 2014, Vol. 46 ›› Issue (8): 136-138.doi: 10.11799/ce201408043

• 信息工程 • 上一篇    下一篇

基于SOM神经网络的智能变电站故障录波启动判据算法

刘建华1,2,李天玉3,蔡儒军2   

  1. 1.
    2. 中国矿业大学
    3. 中国矿业大学信电学院
  • 收稿日期:2014-02-12 修回日期:2014-04-17 出版日期:2014-08-11 发布日期:2014-08-10
  • 通讯作者: 李天玉 E-mail:15162222511@163.com

Based on SOM neural network for recorder starting criteria algorithm of Smart Substation of Coal mine

1,1, 1   

  • Received:2014-02-12 Revised:2014-04-17 Online:2014-08-11 Published:2014-08-10

摘要:

由于传统故障录波启动判据算法具有一定局限性,本文提出一种基于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

Abstract:

As to the limitation of traditional starting criteria for fault recorder algorithm, this article proposes a algorithm based on SOM neural network. An example of A-phase current limit is done in this research of algorithm. The construction of SOM neural network, network training and cluster prediction are completed, then inputting the input vector normalized to the trained SOM model, the output will 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 are inputted into network model, then the outcome shows that the starting criteria for fault recorder algorithm based on SOM neural network has strong adaptive ability and can effectively complete the recording start with minor error.

Key words: smart substation, fault recorder, starting criteria, SOM neural network, cluster analysis