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Article

Detection and Classification of Recessive Weakness in Superbuck Converter Based on WPD-PCA and Probabilistic Neural Network

by 1, 1, 1,* and 2
1
College of Electronic and Information Engineering, Tongji University, No. 4800, Cao’an Highway, Shanghai 201804, China
2
School of Ocean and Earth Science, Tongji University, No.1239, Siping Road, Shanghai 20092, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(3), 290; https://doi.org/10.3390/electronics8030290
Received: 1 February 2019 / Revised: 21 February 2019 / Accepted: 28 February 2019 / Published: 5 March 2019
This paper proposes a detection and classification method of recessive weakness in Superbuck converter through wavelet packet decomposition (WPD) and principal component analysis (PCA) combined with probabilistic neural network (PNN). The Superbuck converter presents excellent performance in many applications and is also faced with today’s demands, such as higher reliability and steadier operation. In this paper, the detection and classification issue to recessive weakness is settled. Firstly, the performance of recessive weakness both in the time and frequency domain are demonstrated to clearly show the actual deterioration of the circuit system. The WPD and Parseval’s theorem are utilized in this paper to feature the extraction of recessive weakness. The energy discrepancy of the fault signals at different wavelet decomposition levels are then chosen as the feature vectors. PCA is also employed to the dimensionality reduction of feature vectors. Then, a probabilistic neural network is applied to automatically detect and classify the recessive weakness from different components on the basis of the extracted features. Finally, the classification accuracy of the proposed classification algorithm is verified and tested with experiments, which present satisfying classification accuracy. View Full-Text
Keywords: fault detection; superbuck converter; wavelet packet decomposition (WPD); principal component analysis (PCA); probabilistic neural network (PNN) fault detection; superbuck converter; wavelet packet decomposition (WPD); principal component analysis (PCA); probabilistic neural network (PNN)
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MDPI and ACS Style

Wu, C.; Yue, J.; Wang, L.; Lyu, F. Detection and Classification of Recessive Weakness in Superbuck Converter Based on WPD-PCA and Probabilistic Neural Network. Electronics 2019, 8, 290. https://doi.org/10.3390/electronics8030290

AMA Style

Wu C, Yue J, Wang L, Lyu F. Detection and Classification of Recessive Weakness in Superbuck Converter Based on WPD-PCA and Probabilistic Neural Network. Electronics. 2019; 8(3):290. https://doi.org/10.3390/electronics8030290

Chicago/Turabian Style

Wu, Chenhao; Yue, Jiguang; Wang, Li; Lyu, Feng. 2019. "Detection and Classification of Recessive Weakness in Superbuck Converter Based on WPD-PCA and Probabilistic Neural Network" Electronics 8, no. 3: 290. https://doi.org/10.3390/electronics8030290

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