摘要
充电模块是电动汽车直流充电桩最为关键的部分,针对其功率开关和电解电容等核心器件的开路故障特点,提出了一种基于小波包变换(wavelet packet transform,WPT)和麻雀搜索算法优化反向传播神经网络(sparrow search algorithm-back propagation,SSA-BP)的故障诊断方法。该方法以充电模块输出电压为原始信号,首先通过预处理剔除其直流分量,对处理后的信号进行小波包分解,然后计算出各子频带信号的能量,通过归一化得到初始特征向量,最后将直流分量和归一化后的特征向量作为最终故障特征量输入到SSA-BP神经网络,进而输出分类结果实现故障诊断。为验证方法的可行性和优越性,搭建了输出功率15 kW的两级仿真模型,在不同工况下进行仿真验证。实验结果表明该方法具有较好的诊断效果,诊断率达到93.85%,对电动汽车直流充电桩故障诊断具有现实指导意义。
Charging modules are the most critical component of electric vehicle DC charging piles.Considering the open circuits fault characteristics of core devices such as power switches and electrolytic capacitors,a fault diagnosis method based on wavelet packet transform(WPT)and sparrow search algorithm-back propagation(SSA-BP)neural networks is proposed.The method takes the output voltage of the charging module as the original signal.Firstly its DC component is rejected through pre-processing,and the processed signal is decomposed into wavelet packet.Then the energy of each sub-band signal is calculated,and the initial feature vec-tor is obtained through normalization.Finally the DC component and the normalized feature vector as the final fault feature quantity are put into the SSA-BP neural network,and then the classification results are output to achieve fault diagnosis.In order to verify the feasibility and superiority of this method,a two-stage simulation model with an output of 15 kW is built under different operating conditions.Experiment results show that this method could effectively improve fault diagnosis accuracy with diagnosis rate of 93.85%.And it has practical guiding significance for fault diagnosis of electric vehicle DC charging piles.
作者
姚望
张英
王明伟
马永超
YAO Wang;ZHANG Ying;WANG Mingwei;MA Yongchao(School of Electrical Engineering,Guizhou University,Guiyang 550025,China;Electric Power Research Institute of Guizhou Power Grid Co.,Ltd.,Guiyang 550002,China;Department of Electrical Engineering,Tsinghua University,Beijing 100084,China)
出处
《南方电网技术》
CSCD
北大核心
2023年第9期85-93,共9页
Southern Power System Technology
基金
国家自然科学基金资助项目(59637050)。
关键词
直流充电桩
充电模块
神经网络
故障诊断
DC charging pile
charging module
neural network
fault diagnosis
