Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems

Majdi Mansouri*, Mansour Hajji, Mohamed Trabelsi, Ayman Al-khazraji, Mohamed Faouzi Harkat, Hazem Nounou, Mohamed Nounou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

In this paper, a new multiscale weighted generalized likelihood ratio test (MS-WGLRT) chart is proposed for enhanced failure detection in photovoltaic systems. The main weakness of the classical generalized likelihood ratio test chart is in dealing with residual samples while ignoring their natural variances. By taking into consideration the nature variance of the detection residual and applying a multiscale representation, the proposed technique allows the reduction in false alarm and missed detection rates compared with the classical generalized likelihood ratio test chart. The multiscale representation of data is an efficient data analysis and feature extraction tool that has a great impact on the effectiveness of failure detection. The effectiveness of the proposed method is evaluated on a simulated photovoltaic data where the developed chart is used for detecting single and multiple failures (eg, bypass, mix, and shading failures). The simulation results show that the multiscale weighted generalized likelihood ratio test method offers better performance compared with the classical generalized likelihood ratio chart.

Original languageEnglish
Article numbere2640
JournalInternational Transactions on Electrical Energy Systems
Volume28
Issue number12
DOIs
Publication statusPublished - Dec 2018
Externally publishedYes

Keywords

  • PV system
  • failure detection (FD)
  • generalized likelihood ratio test (GLRT)
  • multiscale representation
  • weighted GLRT (WGLRT)

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