基于VMD-MPE的开关磁阻电机功率变换器故障诊断

张竞文; 熊立新; 马宏昌; 边敦新

doi:10.13700/j.bh.1001-5965.2020.0696

基于VMD-MPE的开关磁阻电机功率变换器故障诊断

doi: 10.13700/j.bh.1001-5965.2020.0696

张竞文¹,
熊立新^{1, 2, ,},
马宏昌²,
边敦新¹

1.
山东理工大学电气与电子工程学院, 淄博 255000
2.
山东科汇电力自动化股份有限公司, 淄博 255087

基金项目:

国家重点研发计划 2017YFB0902800

详细信息

通讯作者:
熊立新, E-mail: xionglx@sdut.edu.cn

中图分类号: V242.44;TM352
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- 文章访问数: 193
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- PDF下载量: 27
- 被引次数: 0
出版历程
- 收稿日期: 2020-12-16
- 录用日期: 2021-03-13
- 网络出版日期: 2022-06-20
- 整期出版日期: 2022-06-20

Fault diagnosis of switched reluctance motor power converter based on VMD-MPE

1.
School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China
2.
Shandong Kehui Electric Power Automation Co., Ltd., Zibo 255087, China

Funds:

National Key R & D Program of China 2017YFB0902800

More Information

Corresponding author: XIONG Lixin, E-mail: xionglx@sdut.edu.cn

摘要

摘要:
故障诊断是提升开关磁阻电机(SRM)调速系统可靠性的重要技术。针对功率变换器故障信号非线性不稳定、有效信息易被噪声掩盖的特点, 提出了一种新的故障特征提取方法。对直流母线电流进行变分模态分解, 得到若干本征模态分量, 取多尺度有效模态分量排列熵平均值作为特征向量, 输入支持向量机分类器进行故障识别。为验证所提方法的可行性, 建立仿真模型, 与传统的小波分析等故障诊断方法进行对比；搭建了开关磁阻电机实验台架, 测试了开路、短路故障状态。仿真和实验结果表明：所提方法可减小噪声影响, 提高故障识别准确率。
- 开关磁阻电机(SRM) /
- 功率变换器 /
- 故障诊断 /
- 变分模态分解 /
- 多尺度排列熵
Abstract:
Fault diagnosis is an important technology to improve the reliability of the switched reluctance motor (SRM) speed control system. To address the non-linear and unstable fault signal of the switched reluctance motor power converter, and the problem that effective information is easily covered by noise, a new fault feature extraction method is proposed. The DC bus current is subjected to variational mode decomposition to obtain several intrinsic mode functions. The average value of the permutation entropy of the multi-scale effective modal components is taken as the feature vector, and is input into the support vector machine classifier for fault identification. In order to verify the feasibility of the proposed algorithm, a simulation model was established and compared with traditional fault diagnosis algorithms such as wavelet analysis; meanwhile, a switched reluctance motor experiment bench was built to test the open circuit and short circuit fault states. The simulation and experimental results show that the method proposed in this paper can reduce the influence of noise and improve the accuracy of fault identification rate.
- switched reluctance motor (SRM) /
- power converter /
- fault diagnosis /
- variational mode decomposition /
- multiscale permutation entropy

HTML全文

图 1 不对称半桥功率变换器拓扑结构

Figure 1. Asymmetric half-bridge power converter topology

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图 2 正常导通A相电流通路

Figure 2. Current path of phase A under normal conduction

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图 3 功率变换器单相故障

Figure 3. Single phase fault of power converter

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图 4 变分模态分解结果

Figure 4. Variational mode decomposition results

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图 5 采样信号在不同延迟时间下的排列熵

Figure 5. PE of sampled signals under different time delays

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图 6 尺度因子的影响

Figure 6. Influence of scale factor

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图 7 SVM分类结果

Figure 7. SVM classification results

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图 8 EMD-MPE和SWT-MPE分类结果

Figure 8. EMD-MPE and SWT-MPE classification results

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图 9 故障诊断时间对比

Figure 9. Comparison of fault diagnosis time

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图 10 实验系统框图

Figure 10. Experimental system block diagram

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图 11 实验系统

Figure 11. Experiment system

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图 12 实测电流

Figure 12. Measured current

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图 13 直流母线电流有效分量平均排列熵

Figure 13. Average permutation entropy of effective component of DC bus current

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表 1 不同K值对应的中心频率

Table 1. Center frequency of different K values

K值	中心频率/kHz
5	1.33, 0.54, 0.26, 0.08, 0.002
6	1.61, 0.90, 0.47, 0.25, 0.08, 0.002
7	1.99, 1.34, 0.81, 0.45, 0.25, 0.08, 0.002
8	2.13, 1.54, 1.04, 0.67, 0.43, 0.24, 0.08, 0.002
9	2.36, 1.66, 1.54, 0.96, 0.63, 0.42, 0.24, 0.08, 0.002

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表 2 模态分量对应的相关系数

Table 2. Correlation coefficients of modal components

IMF	γ_i
IMF1	0.088 6
IMF2	0.102 7
IMF3	0.126 0
IMF4	0.159 3
IMF5	0.208 5
IMF6	0.310 5
IMF7	0.813 3
IMF8	0.460 6

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表 3 不同诊断方法的识别准确率

Table 3. Recognition accuracy rate of different diagnostic methods

诊断方法	识别准确率/%
EMD-MPE	86.67
SWT-MPE	93.33
VMD-MPE	100

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表 4 噪声环境下分类结果

Table 4. Classification result under noisy environment

诊断方法	识别准确率/%
EMD-MPE	76.67
SWT-MPE	90
VMD-MPE	96.67

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表 5 不同诊断方法分类结果

Table 5. Classification results of different diagnostic methods

诊断方法	故障类型	测试样本数	识别结果			综合识别准确率/%
诊断方法	故障类型	测试样本数	正常	开路	短路	综合识别准确率/%
EMD-MPE	正常	20	18	1	1	80
	开路	20	2	14	4
	短路	20	1	3	16
SWT-MPE	正常	20	19	1	1	91.67
	开路	20	1	19
	短路	20	3		17
VMD-MPE	正常	20	20			100
	开路	20		20
	短路	20			20

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