Online reduced kernel GLRT technique for improved fault detection in photovoltaic systems

R. Fezai, Majdi Mansouri, M. Trabelsi, M. Hajji, H. Nounou, M. Nounou

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper proposes an effective kernel generalized likelihood ratio test (KGLRT) technique for fault detection in Photovoltaic (PV) systems. The proposed technique is considered as an improvement of the conventional KGLRT with extended online capabilities and lower computational complexity. The proposed online reduced KGLRT (OR-KGLRT) is based on transforming the process data into a higher dimensional space (where the data becomes linear), which makes the kernel-based scheme attractive for modeling nonlinear systems. The performance of the proposed method is evaluated and compared to the conventional KGLRT statistic using a simulated PV data. Both techniques are applied to detect single and multiple failures (including Bypass, Mismatch, Mix and Shading failures). The selected performance criteria are the good detection rate (GDR), false alarm rate (FAR), and computation time (CT). Simulation results show superior detection efficiency of the proposed approach compared to the conventional KGLRT statistic in terms of GDR, FAR and CT.

Original languageEnglish
Pages (from-to)1133-1154
Number of pages22
JournalEnergy
Volume179
DOIs
Publication statusPublished - 15 Jul 2019

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Keywords

  • Fault detection
  • Kernel generalized likelihood ratio test (KGLRT)
  • Kernel principal component analysis (KPCA)
  • Online reduced GLRT (OR-GLRT)
  • Photovoltaic (PV) system

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Pollution
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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