CN108416103A - 一种串联混合动力电动汽车交直流变换器的故障诊断方法 - Google Patents
一种串联混合动力电动汽车交直流变换器的故障诊断方法 Download PDFInfo
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Abstract
一种串联混合动力电动汽车交直流变换器的故障诊断方法,实现对交直流变换器电力电子器件开路故障的识别与诊断,包括如下步骤:首先建立串联混合动力电动汽车交直流变换器的仿真模型,并选取直流侧母线输出电流为故障特征量;接着根据发生故障的电力电子器件个数及其位置,对故障类型进行详细分类;然后利用快速傅里叶变换将故障特征量即直流侧母线输出电流分解到不同的频率段,选取不同频率段的谐波含有率为故障诊断特征向量;最后结合基于遗传算法的BP神经网络实现故障类型识别。本发明主要针对电力电子电路功率器件开路故障诊断与识别,计算简便,准确性高,可有效诊断发生故障的电力电子器件的个数与相对位置。
Description
技术领域
本发明涉及电力电子电路故障诊断领域,特别涉及一种串联混合动力电动汽车交直流变换器的故障诊断方法。
背景技术
当前世界汽车产业正处于技术革命和产业大调整的发展时期,安全、环保、节能和智能化成为世界汽车界共同关心的重大课题。混合动力汽车由于其低能耗、低污染和高度自动化等优势,成为汽车界重点研究和开发对象。混合动力电动汽车的电气驱动***主要由发动机、永磁交流同步电动机、发电机、燃料电池以及各相关交直流变换器组成。其中,电动机和发电机所用三相电压型逆变器被集成一个模块上,逆变器普遍采用带有反并联续流二极管的IGBT组成的PWM控制电压型逆变器,这种逆变器具有线路简单、效率较高的特点,使得电动汽车成为电力电子技术一个新的应用领域。
在混合动力电力汽车电气驱动***的正常运行中,对于其任一部分比如发电机、逆变器、整流器或者电动机等若发生故障,都会影响驱动***的可靠运行,如果存在于***中的任何故障不能够及时检出并得到纠正,故障就有可能进一步扩大,致使上层***的状态发生变化,造成功能失效,影响车辆正常运行甚至危及安全。因此,准确、可靠、快速地对驱动***发生的故障进行诊断是提高汽车运行效能的有效措施。
发明内容
本发明要解决的技术问题是,针对现有驱动***故障诊断存在的上述不足,提供一种串联混合动力电动汽车交直流变换器的故障诊断方法,实现对串联混合动力电动汽车交直流变换器的故障诊断与识别,主要针对电力电子电路功率器件开路故障,计算简便,准确性高,可有效诊断发生故障的电力电子器件的个数与相对位置。
本发明为解决上述技术问题所采用的技术方案是:
一种串联混合动力电动汽车交直流变换器的故障诊断方法,包括以下步骤:
(1)建立串联混合动力电动汽车交直流变换器的仿真模型,并选取直流侧母线输出电流为故障特征量;
(2)根据发生故障的电力电子器件个数及其位置,对故障类型进行详细分类;
(3)利用快速傅里叶变换将故障特征量即直流侧母线输出电流分解到不同的频率段,选取不同频率段的谐波含有率为故障诊断特征向量;
(4)结合基于遗传算法的BP神经网络实现故障类型识别。
按上述方案,所述步骤(3)的具体方法如下:通过对正常运行状态与故障状态直流侧母线输出电流波形的快速傅里叶变换分析结果对比,选取f=30kHz(k=0,1,2,3...)谐波含有率构成故障诊断特征向量,其中k=0,1,2,3,…,n,6≤n≤12。
按上述方案,所述步骤(4)的具体方法如下:
1)确定BP神经网络结构
对于BP神经网络的创建,采取三层网络,在三层网络中,隐层神经网络个数n2和输入层神经元个数n1之间有近似关系:
n2=2×n1+1
其中输入层神经元个数n1即故障诊断特征向量的输入参数,n1=n+1,三层神经网络结构为n1-n2-2,共有n1*n2+2*n2个权值,n2+2个阈值;
2)运用遗传算法优化BP神经网络的初始权值和阈值:
遗传算法优化BP神经网络的要素包括种群初始化、适应度函数、选择算子、交叉算子和变异算子,对于种群初始化,个体编码使用二进制编码,由输入层与隐含层连接权值、隐含层阈值、隐含层与输出层连接权值、输出层阈值四部分组成;对于适应度函数,使BP神经网络在预测时,预测值与期望值的残差尽可能小,选择预测样本的预测值与期望值的误差矩阵的范数作为目标函数的输出,以此获得使目标函数值最小时的最优权值和阈值;
3)应用优化后的权值和阈值进行BP神经网络的训练及预测。
与现有技术相比,本发明具有以下有益效果:本发明使用经遗传算法优化后的权值和阈值的BP神经网络预测结果所产生的误差明显要小于使用随机权值和阈值预测结果所产生的误差;该故障诊断方法可有效实现对串联混合动力电动汽车交直流变换器的故障诊断与识别,主要针对电力电子电路功率器件开路故障,保证准确高效的故障识别,并且操作简便。
附图说明
图1为本发明故障诊断方法流程图;
图2为交直流变换器仿真拓扑图;
图3为仿真运行结果误差进化曲线图。
具体实施方式
下面结合附图和较优选实施例对本发明的技术方案进行详细地阐述。以下较优选实施例仅用于说明和解释本发明,而不构成对本发明技术方案的限制。
参照图1所示,本发明一种串联混合动力电动汽车交直流变换器的故障诊断方法,包含以下步骤:
(1)建立串联混合动力电动汽车交直流变换器的仿真模型,由于整流器和逆变器之间存在电容,补偿某些故障引起的电压降和谐波的变化,从而影响故障的正常检测,所以选取直流侧母线输出电流即直流侧电流信号Idc为故障特征量;交直流变换器是串联混合动力电动汽车电气驱动***中实现功率变换以及调速调频的核心装置,仿真拓扑结构见图2,其中交流***电源电压为220V,频率为50Hz;
(2)根据发生故障的电力电子器件个数及其位置,对故障类型进行详细分类,具体如下:
对于交直流变换器而言,最常见的电力故障为电力电子器件开路或者单相接地故障,本发明则重点针对电力电子器件开路产生的故障特征进行分析;对于拓扑结构右侧的三相电压型桥式逆变电路而言,采用IGBT作为开关器件,6个开关器件S1~S6的开关运行状态共计26种,按照开路故障发生的位置及数目以及通过仿真分析得到的直流侧母线输出电流波形将其分为以下几类,如表1所示:
表1交直流变换器的故障种类以及相应故障波形
其中,T表示正常运行,F表示开路故障,F0、F1、F2、F3、F4、F5、F6分别表示开路故障的个数为0、1、2、3、4、5、6。由于3只以及3只以上器件同时发生故障的概率相比其它断路故障发生概率而言较小,在本发明中不予以考虑,所以对于该***所有IGBT器件发生开路故障的情况,可分为以下几类:
G0:正常工作状态,1种情况;
G1:1只器件发生故障,6种情况;
G2:同一相上下两臂2只器件发生故障(比如S1、S4),3种情况;
G3:不同相的2只器件发生故障(比如S1、S6),共12种情况;
G4:3只以及3只以上器件发生故障,在此处不予以分析;
(3)利用快速傅里叶变换将故障特征量即直流侧母线输出电流分解到不同的频率段,选取30kHz(k=1,2,3…)频率段信号为故障诊断特征向量,具体如下:
根据步骤(2)的分析,可知共计可获得22组数据以构造BP神经网络的训练样本,提取故障诊断特征向量时,以30Hz为基波频率,每种故障类型选取部分情况的直流母线电流波形进行FFT分析,提取f=30kHz(k=0,1,2,…12)次谐波含量值作为故障诊断特征向量,表2列出通过对直流侧母线输出电流进行FFT分析得到的样本数据如下:
表2不同故障状态的样本数据
由于本发明实施例共分析四种故障模式,因此可以采取如下的形式来表示各种故障类型:
无故障状态:G0(0,0)
一只器件发生故障:G1(0,1)
同相两只器件发生故障:G2(1,0)
不同相的两只器件发生故障:G3(1,1)
为了对训练好的网络进行测试,另外给出以下几组数据作为网络的测试数据,如表3所列:
表3测试样本数据
(4)结合基于遗传算法的BP神经网络实现故障类型识别,具体如下:
遗传算法优化BP神经网络主要包括三大部分:BP神经网络结构确定、遗传算法优化权值和阈值、BP神经网络训练及预测:
1)确定BP神经网络结构
对于BP神经网络的创建,一般的模式识别问题,三层网络就可以很好地解决问题,根据步骤(3)可知,样本有13个输入参数即输入层神经元个数n1为13,2个输出参数,根据近似关系n2=2×n1+1,可算得隐层神经元个数n2为27,所以设置的三层神经网络结构为13-27-2,则共有13*27+2*27=405个权值,27+2=29个阈值,所以遗传算法优化的参数个数为405+29=434个;另外BP神经网络的隐层神经元的传递函数采用S型正切函数,输出层神经元的传递函数采用S型对数函数;
2)应用遗传算法优化BP神经网络初始权值和阈值
一般情况下,神经网络的权值和阈值是通过随机初始化为[-0.5,0.5]区间的随机数,这个初始化参数对网络训练的影响很大,但又无法准确获得,对于相同的初始权值和阈值,网络的训练结果是一样的,引入遗传算法就是为了优化得出最佳的初始权值和阈值遗传算法优化BP神经网络是用遗传算法来优化BP神经网络的初始权值和阈值。
遗传算法优化BP神经网络的要素包括种群初始化、适应度函数、选择算子、交叉算子和变异算子,对于种群初始化,个体编码使用二进制编码,由输入层与隐含层连接权值、隐含层阈值、隐含层与输出层连接权值、输出层阈值四部分组成;对于适应度函数,本发明是为了使BP神经网络在预测时,预测值与期望值的残差尽可能小,所以选择预测样本的预测值与期望值的误差矩阵的范数作为目标函数的输出。
在本发明中,假定权值和阈值的编码均为10位二进制数,那么个体的二进制编码长度为4340,具体运行参数见表4:
表4遗传算法运行参数设定
种群大小 | 最大遗传代数 | 变量的二进制位数 | 交叉概率 | 变异概率 | 代沟 |
40 | 50 | 10 | 0.7 | 0.01 | 0.95 |
3)应用优化后的权值和阈值进行BP神经网络的训练及预测
对于BP神经网络的训练和测试,神经网络训练数据的过程就是不断调整未知参数使得代价函达到最小值,训练函数是利用莱文贝格-马夸特(Levenberg-Marquardt)算法对网络进行训练,不断修正权值和阈值,使得网络的输出误差最小,保证预测结果的准确性。
根据遗传算法和BP神经网络理论,在MATLAB软件中编程实现基于遗传算法优化的BP神经网络故障诊断方法。其中,遗传算法部分使用Sheffield遗传算法工具箱,BP神经网络部分使用MATLAB自带的神经网络工具箱。运行结果见图3,该算法的输出结果为优化后的权值和阈值矩阵以及预测结果的最小误差。由图3可知,应用遗传算法优化后的BP神经网络预测结果的最小误差为0.033。
现将使用随机权值和阈值的BP神经网络预测结果和使用优化后权值和阈值的测试样本预测结果进行对比如下:
表5仿真结果对比表
由表5可知,使用经遗传算法优化后的权值和阈值的BP神经网络预测结果所产生的误差明显要小于使用随机权值和阈值预测结果所产生的误差。所以使用经遗传算法优化后的神经网络算法,可有效无误诊断该电子电路所有器件的开路故障类型。
以上内容结合附图对本发明进行了示例性说明,在结构和布局方面还有多种变化和变型,因此等同的技术方案也属于本发明的范畴,采用本发明的构思和方案的非实质性改进,均在本发明的保护范围之内。
Claims (3)
1.一种串联混合动力电动汽车交直流变换器的故障诊断方法,其特征在于,包括以下步骤:
(1)建立串联混合动力电动汽车交直流变换器的仿真模型,并选取直流侧母线输出电流为故障特征量;
(2)根据发生故障的电力电子器件个数及其位置,对故障类型进行详细分类;
(3)利用快速傅里叶变换将故障特征量即直流侧母线输出电流分解到不同的频率段,选取不同频率段的谐波含有率为故障诊断特征向量;
(4)结合基于遗传算法的BP神经网络实现故障类型识别。
2.根据权利要求1所述的一种串联混和动力汽车交直流变换的故障诊断方法,其特征在于,所述步骤(3)的具体方法为:通过对正常运行状态与故障状态直流侧母线输出电流波形的快速傅里叶变换分析结果对比,选取f=30kHz谐波含有率构成故障诊断特征向量,其中k=0,1,2,3,…,n,6≤n≤12。
3.根据权利要求1所述的一种串联混和动力汽车交直流变换的故障诊断方法,其特征在于,所述步骤(4)的具体方法为:
1)确定BP神经网络结构
对于BP神经网络的创建,采取三层网络,在三层网络中,隐层神经网络个数n2和输入层神经元个数n1之间有近似关系:
n2=2×n1+1
其中输入层神经元个数n1即故障诊断特征向量的输入参数,n1=n+1,三层神经网络结构为n1-n2-2,共有n1*n2+2*n2个权值,n2+2个阈值;
2)运用遗传算法优化BP神经网络的初始权值和阈值:
遗传算法优化BP神经网络的要素包括种群初始化、适应度函数、选择算子、交叉算子和变异算子,对于种群初始化,个体编码使用二进制编码,由输入层与隐含层连接权值、隐含层阈值、隐含层与输出层连接权值、输出层阈值四部分组成;对于适应度函数,为了使BP神经网络在预测时,预测值与期望值的残差尽可能小,选择预测样本的预测值与期望值的误差矩阵的范数作为目标函数的输出,以此获得使目标函数值最小时的最优权值和阈值;
3)应用优化后的权值和阈值进行BP神经网络的训练及预测。
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