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

1 Citation (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 publication2017 IEEE 44th Photovoltaic Specialist Conference, PVSC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781509056057
DOIs
Publication statusPublished - 25 May 2018
Event44th IEEE Photovoltaic Specialist Conference, PVSC 2017 - Washington, United States
Duration: 25 Jun 201730 Jun 2017

Other

Other44th IEEE Photovoltaic Specialist Conference, PVSC 2017
CountryUnited States
CityWashington
Period25/6/1730/6/17

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ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

Cite this

Wang, Z., & Balog, R. (2018). Arc fault and flash detection in photovoltaic systems using wavelet transform and support vector machines. In 2017 IEEE 44th Photovoltaic Specialist Conference, PVSC 2017 (pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PVSC.2017.8366770