基于小波奇异熵和SOM神经网络的微电网系统故障诊断  

Fault diagnostic method for micro-grid based on wavelet singularity entropy and SOM neural network

作　　者：邱路[1] 叶银忠[2] 姜春娣[3] 

机构地区：[1]上海海事大学物流工程学院,上海201306 [2]上海应用技术大学电气与电子工程学院,上海201418 [3]衢州学院电气与信息工程学院,浙江衢州324000

出　　处：《山东大学学报（工学版）》2017年第5期118-122,129,共6页Journal of Shandong University（Engineering Science）

基　　金：国家自然科学基金资助项目(61374132);浙江省公益基金资助项目(2016C31SA901322);上海海事大学研究生创新基金资助项目(2014ycx057)

摘　　要：针对微电网系统运行方式灵活、拓扑结构多样的特点,基于对小波变换、奇异值分解和泛化信息熵基本理论的分析,揭示了小波奇异熵能够对故障信号给出确定的量度,将小波奇异熵与自组织特征映射(self-organizing feature map,SOM)神经网络相结合,提出一种能够适应微电网系统拓扑结构变化情况的故障诊断方法。利用PSCAD4.2建立了微电网故障仿真系统,进行故障诊断仿真试验。试验结果表明:该方法不受故障位置、故障时刻等因素的影响,在微电网系统拓扑结构发生变化的情况下,能实现有效的故障诊断。According to the diversity of micro grid＇s topology, through analyzing the theories of wavelet transform, singular value decomposition and extended shannon-entropy, the wavelet singular entropy could measure the fault signal. A fault diagnosis method for the micro grid system was proposed by integrating the wavelet singular entropy with the self organizing feature map （SOM） neural network. A micro grid fault simulation system was established by PSCAD4. 2. The simulation results proved that the proposed diagnosis method was insensitive to the location and the time fault occurs, which had strong adaptability to the variation in structure topology.

关 键 词：小波奇异熵 SOM神经网络 微电网 拓扑结构 故障诊断 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]