Next Article in Journal
Comparison of Different Algorithms to Orthorectify WorldView-2 Satellite Imagery
Next Article in Special Issue
Evaluation of Cloud Services: A Fuzzy Multi-Criteria Group Decision Making Method
Previous Article in Journal
Local Convergence Analysis of an Eighth Order Scheme Using Hypothesis Only on the First Derivative
Previous Article in Special Issue
Noise Reduction of Steel Cord Conveyor Belt Defect Electromagnetic Signal by Combined Use of Improved Wavelet and EMD
Article Menu

Export Article

Open AccessArticle
Algorithms 2016, 9(4), 66; doi:10.3390/a9040066

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
Received: 25 August 2016 / Revised: 30 September 2016 / Accepted: 30 September 2016 / Published: 11 October 2016
View Full-Text   |   Download PDF [2112 KB, uploaded 11 October 2016]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top