Fault Detection in Photovoltaic Arrays via Sparse Representation Classifier

Heybet Kilic, Behnam Khaki, Bilal Gumus, Musa Yilmaz, Peter Palensky

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)
27 Downloads (Pure)


In recent years, there has been an increasing interest in the integration of photovoltaic (PV) systems in the power grids. Although PV systems provide the grid with clean and renewable energy, their unsafe and inefficient operation can affect the grid reliability. Early stage fault detection plays a crucial role in reducing the operation and maintenance costs and provides a long lifespan for PV arrays. PV Fault detection, however, is challenging especially when DC short circuit occurs under the low irradiance conditions while the arrays are equipped with an active maximum power point tracking (MPPT) mechanism. In this case, the efficiency and power output of a PV array decrease significantly under hard-to-detect faults such as active MPPT and low irradiance. If the hard-to-detect faults are not detected effectively, they will lead to PV array damage and potential fire hazard. To address this issue, in this paper we propose a new sparse representation classifier (SRC) based on feature extraction to effectively detect DC short circuit faults of PV array. To verify the effectiveness of the proposed SRC fault detection method, we use numerical simulation and compare its performance with the artificial neural network (ANN) based fault detection.

Original languageEnglish
Title of host publication2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)
Subtitle of host publicationProceedings
Place of PublicationPiscataway
Number of pages7
ISBN (Electronic)978-1-7281-5635-4
ISBN (Print)978-1-7281-5636-1
Publication statusPublished - 2020
Event29th IEEE International Symposium on Industrial Electronics - Delft, Netherlands
Duration: 17 Jun 202019 Jun 2020


Conference29th IEEE International Symposium on Industrial Electronics
Abbreviated titleISIE 2020
OtherVirtual/online event due to COVID-19
Internet address

Bibliographical note

Virtual/online event due to COVID-19
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Compressive sensing
  • Photovoltaic array fault detection
  • sparse representation.


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