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Open AccessArticle

Fault Sensing Using Fractal Dimension and Wavelet

School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Author to whom correspondence should be addressed.
Academic Editors: Javier Del Ser Lorente and Hsiung-Cheng Lin
Algorithms 2016, 9(4), 66;
Received: 25 August 2016 / Revised: 30 September 2016 / Accepted: 30 September 2016 / Published: 11 October 2016
PDF [2112 KB, uploaded 11 October 2016]


A new fusion sensing (FS) method was proposed by using the improved fractal box dimension (IFBD) and a developed maximum wavelet coefficient (DMWC) for fault sensing of an online power cable. There are four strategies that were used. Firstly, the traditional fractal box dimension was improved to enlarge the feature distances between the different fault classes. Secondly, the IFBD recognition algorithm was proposed by using the improved fractal dimension feature extracted from the three-phase currents for the first stage of fault recognition. Thirdly, the DMWC recognition algorithm was developed based on the K-transform and wavelet analysis to establish the relationship between the maximum wavelet coefficient and the fault class. Fourthly, the FS method was formed by combining the IFBD algorithm and the DMWC algorithm in order to recognize the 10 types of short circuit faults of online power. The designed test system proved that the FS method increased the fault recognition accuracy obviously. In addition, the parameters of the initial angle, transient resistance, and fault distance had no influence on the FS method. View Full-Text
Keywords: fusion sensing; fault recognition; feature extraction; fractal dimension; wavelet; power cable fusion sensing; fault recognition; feature extraction; fractal dimension; wavelet; power cable

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Wang, M.; Zhu, L.; Guo, Y. Fault Sensing Using Fractal Dimension and Wavelet. Algorithms 2016, 9, 66.

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