Line-to-Line Faults Detection for Photovoltaic Arrays Based on I-V Curve Using Pattern Recognition

Aref Eskandari, Jafar Milimonfared, Mohammadreza Aghaei, Aline Kirsten Vidal De Oliveira, Ricardo Ruther

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

Fault detection plays a crucial role in reliability and safety of photovoltaic systems. However, the fault detection by the conventional protection devices is always difficult due to nonlinear characteristics of PV systems, Maximum Power Point Tracking (MPPT), low irradiation, and high fault impedance. In addition, it may lead to the power losses, efficiency reduction and even fire hazard. This paper proposes an innovative fault detection method based on the pattern recognition techniques and extraction of the essential features from the current-voltage (I-V) characteristics. The main benefit of this method is using less data to detect faults while improving accuracy. The primary results demonstrate that the proposed method is accurate, effective and reliable for detecting line-line faults in PV systems.

Original languageEnglish
Title of host publication2019 IEEE 46th Photovoltaic Specialists Conference, PVSC 2019
PublisherInstitute of Electrical and Electronics Engineers
Pages503-507
Number of pages5
ISBN (Electronic)9781728104942
DOIs
Publication statusPublished - Jun 2019
Externally publishedYes
Event46th IEEE Photovoltaic Specialists Conference, PVSC 2019 - Chicago, United States
Duration: 16 Jun 201921 Jun 2019

Conference

Conference46th IEEE Photovoltaic Specialists Conference, PVSC 2019
CountryUnited States
CityChicago
Period16/06/1921/06/19

Keywords

  • Fault Detection
  • Line-Line Fault
  • Machine Learning Algorithm
  • Pattern Recognition
  • Photovoltaic System

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