Dynamic Feature Eztraction of Power Disturbance Signal Based on Time-frequency Technology

摘要：

The growing concern for power quality issues from both utilities and power users is generated by proliferation of power electronic devices and nonlinear loads in power system network. Therefore, the techniques for power quality monitoring and power disturbance mitigation are capturing increasing attention. A novel approach for the power quality disturbances recognition using wavelet transform and neural network is proposed. The wavelet transform is used to complete feature extraction and can accurately localizes the characteristics of transient signal both in time and frequency domains. These feature vectors are input variables for neural network training and the neural network structure is designed for disturbance pattern recognition. Therefore, the wavelet network combines advantages of wavelet transformation for purposes of feature extraction and selection with the characteristic decision capabilities of neural network approaches. During the training process, the wavelet network learns adequate decision functions and arbitrarily complex decision regions defined by the weight coefficients. The simulation results demonstrate the proposed method gives a new way for signal analysis and pattern recognition of power quality disturbances.

关键词： Power quality disturbance wavelet transform feature eztraction neural network pattern recognition training algorithm

作者: Wang Yuguo Zhao Wei Xie Yan

作者单位: Hebei University of Engineering, Handan 056038, China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 2300-2303

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）