Fault classification method for inverter based on hybrid support vector machines and wavelet analysis |
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Authors: | Zhi-kun Hu Wei-hua Gui Chun-hua Yang Peng-cheng Deng and Steven X Ding |
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Affiliation: | (1) Raja College of Engineering and Technology, Madurai, 625 020, Tamil Nadu, India;(2) National Engineering College, Kovilpatti, Tamil Nadu, India;(3) Thiagarajar College of Engineering, Madurai, Tamil Nadu, India |
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Abstract: | A new classification method for fault waveform is proposed based on discrete orthogonal wavelet transform (DOWT) and hybrid
support vector machine (hybrid SVM) for fault type of a three-phase voltage inverter. The waveforms of output voltage obtained
from the faulty inverter are decomposed by DOWT into wavelet coefficient matrices, through which we can obtain singular value
vectors acted as features of time-series periodic waveforms. And then a multi-classes classification method based on a new
Huffman Tree structure is presented to realize 1-v-r SVM strategy. The extracted features are applied to hybrid SVM for determining
fault type. Compared to employing the structure based on ordinary binary tree, the superiority of the proposed SVM method
is shown in the success of fault diagnosis because the average Loo-correctness of the SVM based on Huffman tree structure
exceed the general SVM 3.65%, and the correctness reaches 99.6%. |
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Keywords: | |
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