【摘要】 针对光伏并网逆变器电路中故障信号的非线性、非平稳特点,提出一种基于经验模态分解(EMD)和样本熵(SampEn)的故障诊断方法;首先,利用经验模态分解对逆变器的三相输出电压进行分解,得到有限个本征模式分量(IMF),从中选取包含故障主要信息的前几个本征模式分量提取故障信息;然后,计算本征模式分量的样本熵,从而得到用于故障诊断的特征向量;最后,将逆变器开路故障进行分类和编码,将故障特征向量输入BP神经网络进行模式识别,从而达到故障诊断的目的;在Matlab环境下对光伏并网逆变器的故障诊断进行了实验,实验结果证明了文中方法能实现对光伏并网逆变器的故障诊断,且与小波包变换相比,该方法具有诊断效率高和准确度高等特点。更多还原

【Abstract】 Aiming at the fault signal of the grid—connected photovoltaic inverter having the problems such as nonlinear and non—stationary,a faults diagnosis method based on empirical mode decomposition and sample entropy is proposed.Firstly,the output three—phase voltages of inverter are decomposed into a series of intrinsic mode functions(IMF) by empirical mode decomposition(EMD) and then the intrinsic mode functions containing the most information are chosen to extract fault informations Secondly,calculating the sample entropy of the intrinsic mode functions and the fault diagnosis feature vectors are obtained.Finally,The break faults of inverter are classified and coded.The feature vectors are acted as inputs of the BP neural network for pattern recognition in order to achieve the goal of fault diagnosis.The experiment is implemented in the Matlab environment,The experiment result proves that the method in this paper can realize the fault diagnosis,and compared with the traditional wavelet packet transform,it has the higher diagnosis efficiency and accuracy.更多还原