首页> 外文会议>International conference on intelligent systems and knowledge engineering >Study on Transient Signals Recognition in Power System Based on Multiwavelet Packet Coefficient Entropy and Artificial Neural Network
【24h】

Study on Transient Signals Recognition in Power System Based on Multiwavelet Packet Coefficient Entropy and Artificial Neural Network

机译:基于多小波包系数熵和人工神经网络的电力系统瞬态信号识别研究

获取原文

摘要

Multiwavelets own better properties than those of traditional wavelets. In the paper multiwavelet packet coefficient entropy (MPCE) is defined through combining decomposition coefficient of multiwavelet packet with entropy. A novel transient signals recognition method based on MPCE and artificial neural network (ANN) is proposed. Firstly, the appropriate multiwavelet packet decomposition of the sampled transient current signal is performed and each MPCE of transient current is calculated. Then eigenvector of multiwavelet packet of the current signal is constructed, and by taking the eigenvector as training samples the radial basis function (RBF) neural network is trained to implement the transient signals recognition. At last the proposed method is compared with the means based on traditional wavelet packet and ANN. Simulation results show that the proposed method is effective and feasible and the recognition capability is better than the method based on traditional wavelet packet and ANN.
机译:多小波拥有比传统小波更好的性能。在纸张中,通过将多小波包的分解系数与熵组合来限定纸张多小波分组系数熵(MPCE)。提出了一种基于MPCE和人工神经网络(ANN)的新型瞬态信号识别方法。首先,执行采样的瞬态电流信号的适当的多灯分组分解,并计算瞬态电流的每个MPCE。然后,构造电流信号的多小波包的特征向量,并且通过将特征向量作为训练样本进行径向基函数(RBF)神经网络以实现瞬态信号识别。最后,将所提出的方法与基于传统小波包和ANN的手段进行比较。仿真结果表明,该方法是有效的,可行的方法,识别能力优于基于传统小波包和ANN的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号