Abstract
Power electronic circuits (PECs) are prone to various failures, whose
classification is of paramount importance. This paper presents a
data-driven based fault diagnosis technique, which employs a support
vector data description (SVDD) method to perform fault classification of
PECs. In the presented method, fault signals (e.g. currents, voltages,
etc.) are collected from accessible nodes of circuits, and then signal
processing techniques (e.g. Fourier analysis, wavelet transform, etc.) are
adopted to extract feature samples, which are subsequently used to perform
offline machine learning. Finally, the SVDD classifier is used to
implement fault classification task. However, in some cases, the
conventional SVDD cannot achieve good classification performance, because
this classifier may generate some so-called refusal areas (RAs), and in
our design these RAs are resolved with the one-against-one support vector
machine (SVM) classifier. The obtained experiment results from simulated
and actual circuits demonstrate that the improved SVDD has a
classification performance close to the conventional one-against-one SVM,
and can be applied to fault classification of PECs in practice.
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