| 引用本文: | 孟安波,葛佳菲,李德强,等.基于纵横交叉算法的神经网络配电网故障选线研究[J].电力系统保护与控制,2016,44(21):90-95.[点击复制] |
| MENG Anbo,GE Jiafei,LI Deqiang,et al.Research on fault line selection of distribution network using RBF neural network based on crisscross optimization algorithm optimization[J].Power System Protection and Control,2016,44(21):90-95[点击复制] |
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| 摘要: |
| 为了提高小电流接地系统单相接地故障选线的精度，提出一种基于纵横交叉算法优化RBF神经网络的故障选线新方法。利用Matlab/Simulink仿真单相接地得到一组零序电流信号，通过小波包变换和傅里叶变换从中提取出暂态特征值、有功分量以及五次谐波分量。再将提取得到的特征量作为神经网络的输入，用纵横交叉算法优化后的神经网络对故障特征值进行训练，实现故障选线。仿真中建立100组不同的故障样本，其中80组作为训练集，20组作为测试集。实验结果表明，与传统神经网络相比,CSO-RBF方法训练效果好，准确性高。 |
| 关键词: 小波包变换 纵横交叉法 输电线路故障诊断 RBF神经网络 局部最优 |
| DOI：10.7667/PSPC151888 |
| 投稿时间：2015-10-26修订日期：2016-01-27 |
| 基金项目:广东省电网公司科技项目(GDKJ00000009) |
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| Research on fault line selection of distribution network using RBF neural network based on crisscross optimization algorithm optimization |
| MENG Anbo,GE Jiafei,LI Deqiang,WENG Zihao,JIAO Xianan |
| (Guangzhou University of Technology, Guangzhou 510006, China) |
| Abstract: |
| In order to improve the precision of single phase to earth fault line selection device for the small current grounding system, an improved fault line detection method using RBF neural network based on crisscross optimization algorithm (CSO) optimization is presented. By means of Matlab/Simulink, numerous single-phase-to-earth fault experiments are carried out and many zero sequence current signals are obtained. The fault characteristics of the zero sequence current signals, such as transient characteristic value, active component and five harmonic component, are extracted by wavelet packet transform and Fourier transform. Then choosing the extracted characteristic quantity as the input of neural network, using neural network which has been optimized by CSO to train fault characteristic value, the fault line selection can be realized. Through the simulation, 100 sets of different fault samples are set up, and the 80 groups are set as training sets, and the 20 group is the test set. The simulation results indicate that compared with the traditional neural network, the proposed method has better training effect and higher precision. |
| Key words: wavelet packet transform crisscross optimization algorithm transmission line fault diagnosis RBF neural network local optimum |
