A Robust Data-Driven Approach for Fault Detection in Photovoltaic Arrays

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

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

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n this paper, a robust data-driven method for fault detectionin photovoltaic (PV) arrays is proposed. Our method is based onthe random vector functional-link networks (RVFLN) which has theadvantage of randomly assigning hidden layer parameters with no tuning. To eliminate the effect of measurement noise and overfitting in thetraining process which reduce the fault detection accuracy, the sparseregularization method is utilized which uses l2−norm with loss weighting factor to compute the output weights. To attain a strong robustnessagainst the outlier samples, the non-parametric kernel density estimationis employed to assign a loss weighting factor. Through rigorous simulation studies, we validate the performance of our proposed method in detectingthe short and open circuit faults based on only the output current andvoltage measurements of PV arrays. In addition to a stronger robustnesscomparing with the least square-support vector machine, we also showthat our proposed method provides 80% and 100% average detection accuracy for short circuit and open circuit, respectively.
Original languageEnglish
Title of host publication2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) - USB Proceedings
Publication statusPublished - 2021
Event10th IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020 - Virtual/online event due to COVID-19, Delft, Netherlands
Duration: 26 Oct 202028 Oct 2020
Conference number: 10


Conference10th IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020
Abbreviated titleISGT-Europe 2020
OtherVirtual/online event due to COVID-19
Internet address


  • Canonical correlation analysis
  • fault detection
  • photovoltaic array
  • random vector-link network
  • sparse-regularization


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