Discrete wavelet transform optimal parameters estimation for arc fault detection in low-voltage residential power networks

Pan Qi, Slavisa Jovanovic, Jinmi Lezama, Patrick Schweitzer

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

In this work, an in-depth analysis providing the optimal parameters estimation for discrete wavelet transform (DWT) applied to detection of series arc faults in the household AC power network is presented. The influence of three parameters was investigated: the choice of mother wavelet, level of decomposition and sampling frequency. The line current was used as input for all analyses. A performance criterion based on the energy computation of line currents with and without arc faults was defined and used to compare the influence of 550 combinations of these three parameters on the arc fault detection performances for different loads, including two household appliances. The study showed that the right choice of these three parameters greatly influences the arc fault detection performances. Moreover, for each tested load a frequency range providing maximal arc fault detection performances is identified. The study showed also that the choice of the mother wavelet is less critical than the two other parameters.

Original languageEnglish
Pages (from-to)130-139
Number of pages10
JournalElectric Power Systems Research
Volume143
DOIs
StatePublished - 1 Feb 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016 Elsevier B.V.

Keywords

  • Arcing fault
  • Discrete wavelet transform (DWT)
  • Household power network
  • Series arc fault detection

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