【作者】 张东中； 袁帅； 佟为明；

【Author】 Zhang Dongzhong~1 Yuan Shuai~2 Tong Weiming~2 (1 Heilongjiang University Harbin 150080 China;2 Harbin Institute of Technology Harbin 150001 China)

【机构】 黑龙江大学； 哈尔滨工业大学 电气工程系；

【摘要】 为解决对电能质量扰动进行实时监测和自动识别的问题,提出一种基于复小波变换和神经网络的电能质量扰动识别与分类方法。复小波变换采用Db4正交紧支复小波,主要用于提取动态电能质量扰动的特征向量。神经网络主要用于根据提取的特征向量对动态电能质量扰动进行识别和分类。仿真和测试结果验证了该方法是正确和有效的,且具有较高的正确识别率。更多还原

【Abstract】 In order to solve the problem of the power quality disturbances in real-time monitoring and automatic identification,this paper proposes a identification and classification method of power quality disturbances based on complex wavelet transform and artificial neural networks.For extracting the eigenvector of the dynamic power quality disturbances,Db4 orthogonal compact support complex wavelet of complex wavelet transform is used. According to the extracted eigenvector,the dynamic power quality disturbances are identified and classified through neural network.Simulation and test results show that the method is correct and effective,and has a high rate of correct identification.更多还原