Arc fault signal detection - Fourier transformation vs. wavelet decomposition techniques using synthesized data

Zhan Wang, Stephen McConnell, Robert Balog, Jay Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Citations (Scopus)

Abstract

Arc faults are a significant reliability and safety concern for photovoltaic (PV) systems and can cause intermittent operation, system failure, electrical shock hazard, and even fire. Further, arc faults in deployed systems are seemingly random and challenging to faithfully create experimentally in the laboratory, which makes the study of arc fault signature detection difficult. While it may seem trivial to simply record arcing signatures from real-world system, an obstacle in capturing these arc signals is that arc faults in the PV systems do not happen predictably, and depending on the location of the sensors relative to the arc location, may contribute a negligible portion to the magnitude of the sensed current or voltage waveform. The high-frequency content of the arc requires fast sampling, long memory, and fast processing to acquire, store, and analyze the waveforms; this adds substantial balance-of-system cost when considering widespread deployment of arc fault detectors in PV applications. In this paper, we study the performance of the fast Fourier transform arc detection method compared to the wavelet decomposition method by using synthetic waveforms. These waveforms are created by combining measured waveforms of normal background noise from inverters in DC PV arrays along with waveforms of arcing events. Using this technique allows the ratio of amplitudes are varied. Combining these separate waveforms in various amplitude proportions enables creation of test signals for the study of detection algorithm efficacy. It will be shown that the wavelet transformation technique produce more easily recognized detection results and can perform this detection using a much lower sampling rate than what is required for the fast Fourier transform

Original languageEnglish
Title of host publication2014 IEEE 40th Photovoltaic Specialist Conference, PVSC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3239-3244
Number of pages6
ISBN (Electronic)9781479943982
DOIs
Publication statusPublished - 15 Oct 2014
Externally publishedYes
Event40th IEEE Photovoltaic Specialist Conference, PVSC 2014 - Denver
Duration: 8 Jun 201413 Jun 2014

Other

Other40th IEEE Photovoltaic Specialist Conference, PVSC 2014
CityDenver
Period8/6/1413/6/14

    Fingerprint

Keywords

  • arc fault detection
  • filter banks
  • Fourier transform
  • inverter noise
  • wavelet transform

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

Cite this

Wang, Z., McConnell, S., Balog, R., & Johnson, J. (2014). Arc fault signal detection - Fourier transformation vs. wavelet decomposition techniques using synthesized data. In 2014 IEEE 40th Photovoltaic Specialist Conference, PVSC 2014 (pp. 3239-3244). [6925625] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PVSC.2014.6925625