Arc fault and flash detection in photovoltaic systems using wavelet transform and support vector machines

Zhan Wang, Robert Balog

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

9 Citations (Scopus)

Abstract

Arc faults pose significant reliability and safety issues in today's photovoltaic (PV) systems. This paper presents an effective method based on wavelet transform and support vector machines (SVM) for detection of arc faults in DC PV systems. Because of its advantages in time-frequency signal processing, wavelet transform is applied to extract the characteristic features from system voltage/current signals. SVM is then used to identify arc faults. The performance of the proposed technique is compared with traditional Fourier transform based approaches.

Original languageEnglish
Title of host publication2016 IEEE 43rd Photovoltaic Specialists Conference, PVSC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3275-3280
Number of pages6
Volume2016-November
ISBN (Electronic)9781509027248
DOIs
Publication statusPublished - 18 Nov 2016
Externally publishedYes
Event43rd IEEE Photovoltaic Specialists Conference, PVSC 2016 - Portland, United States
Duration: 5 Jun 201610 Jun 2016

Other

Other43rd IEEE Photovoltaic Specialists Conference, PVSC 2016
CountryUnited States
CityPortland
Period5/6/1610/6/16

    Fingerprint

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

Wang, Z., & Balog, R. (2016). Arc fault and flash detection in photovoltaic systems using wavelet transform and support vector machines. In 2016 IEEE 43rd Photovoltaic Specialists Conference, PVSC 2016 (Vol. 2016-November, pp. 3275-3280). [7750271] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PVSC.2016.7750271