Fault Detection of a Proposed Three-Level Inverter Based on a Weighted Kernel Principal Component Analysis

Mao Lin; Ying-Hui Li; Liang Qu; Chen Wu; Guo-Qiang Yuan

Fault Detection of a Proposed Three-Level Inverter Based on a Weighted Kernel Principal Component Analysis

Vol. 16, No. 1, pp. 182-189, Jan. 2016

10.6113/JPE.2016.16.1.182

Fault detection

Kernel principal component analysis (KPCA)

Sensitivity analysis

Three-level inverter

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Abstract

Fault detection is the research focus and priority in this study to ensure the high reliability of a proposed three-level inverter. Kernel principal component analysis (KPCA) has been widely used for feature extraction because of its simplicity. However, highlighting useful information that may be hidden under retained KPCs remains a problem. A weighted KPCA is proposed to overcome this shortcoming. Variable contribution plots are constructed to evaluate the importance of each KPC on the basis of sensitivity analysis theory. Then, different weighting values of KPCs are set to highlight the useful information. The weighted statistics are evaluated comprehensively by using the improved feature eigenvectors. The effectiveness of the proposed method is validated. The diagnosis results of the inverter indicate that the proposed method is superior to conventional KPCA.

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Cite this article

[IEEE Style]

M. Lin, Y. Li, L. Qu, C. Wu, G. Yuan, "Fault Detection of a Proposed Three-Level Inverter Based on a Weighted Kernel Principal Component Analysis," Journal of Power Electronics, vol. 16, no. 1, pp. 182-189, 2016. DOI: 10.6113/JPE.2016.16.1.182.

[ACM Style]

Mao Lin, Ying-Hui Li, Liang Qu, Chen Wu, and Guo-Qiang Yuan. 2016. Fault Detection of a Proposed Three-Level Inverter Based on a Weighted Kernel Principal Component Analysis. Journal of Power Electronics, 16, 1, (2016), 182-189. DOI: 10.6113/JPE.2016.16.1.182.