Fast Model-Based Fault Detection in Single-Phase Photovoltaic Systems

Simon Mayr, Gernot Grabmair, Johann Reger

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

2 Citations (Scopus)

Abstract

We present a model-based approach for the instant detection of faults on the DC side of photovoltaic (PV) systems. The algorithm does not identify the faults itself, but estimates the nominal PV system behavior, i.e. system parameters, using simple PV and line models. Sudden deviations from the expected model behavior serve as an indicator for the ignition of a fault. To ensure that the PV model parameters can be estimated, an identifiability analysis has to be performed. The performance of the algorithm is demonstrated exemplarily by the detection of serial electric arcs in PV systems. Measurement results show that all series arc faults are successfully detected. There are no false detections due to maximum power point tracking (MPPT) operations or environmental influences like shading, changes in solar irradiation, etc. The main advantages of the presented method are less computational effort, resulting in very fast detection times, and its flexible integration into existing systems.

Original languageEnglish
Title of host publicationIECON
Subtitle of host publicationIECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
Pages4615-4622
Number of pages8
ISBN (Electronic)9781728148786
DOIs
Publication statusPublished - Oct 2019
Event45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal
Duration: 14 Oct 201917 Oct 2019

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
Volume2019-October

Conference

Conference45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
Country/TerritoryPortugal
CityLisbon
Period14.10.201917.10.2019

Keywords

  • fault detection
  • parameter estimation
  • photovoltaic systems

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