EMI Diagnostics of Three Phase Inverters Using Machine Learning Algorithms

被引：0

作者：

Boubin, Matthew ^{[1
]}

Doran, John Patrick ^{[1
]}

Guo, Wilson ^{[1
]}

Rajasekhar, Yamuna ^{[1
]}

Scott, Mark ^{[1
]}

机构：

[1] Miami Univ, Dept Elect & Comp Engn, Oxford, OH 45056 USA

来源：

2018 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE) | 2018年

关键词：

electromagnetic interference; machine learning; diagnostics; DC link capacitor; support vector machine; common mode noise; differential mode noise;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Electromagnetic interference (EMI) is often regarded as a disturbance in electrical systems, and most research focuses on developing techniques to mitigate its impact. EMI is particularly problematic in many sensing applications, but there are circumstances when EMI can be used as the sensing tool. This paper describes a method to determine the health of the DC link capacitance in a three-phase inverter using EMI diagnostics. The proposed approach relies on changes in a capacitor's resonant frequency and impedance, which occur naturally as it ages, to predict its health. These parameters influence the conducted EMI measurements in a manner that is detectable. Through simulation and experimentation, it is demonstrated that this approach can detect changes in a capacitor's health via a machine learning algorithm that is trained to inverters EMI spectrum.

引用

页码：4062 / 4069

页数：8

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