An effective statistical fault detection technique for grid connected photovoltaic systems based on an improved generalized likelihood ratio test

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

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

This paper proposes an improved statistical failure detection technique for enhanced monitoring capabilities of PV systems. The proposed technique offers reduced false alarm and missed detection rates compared to the generalized likelihood ratio test (GLRT) by taking into consideration the nature variance of the GLRT statistics and applying a multiscale representation. The multiscale nature of the data provides better robustness to noises and better monitoring quality. The effectiveness of the proposed multiscale weighted GLRT (MS-WGLRT) method in detecting failures is evaluated using a set of synthetic and simulated PV data where the developed chart is used for detecting single and multiple failures (e.g., Bypass, Mix and Shading failures). Moreover, a set of real-data was used in order to prove the effectiveness of the proposed technique in detecting partial shading faults. All results show that the MS-WGLRT method offers better fault detection performances compared to the classical WGLRT and conventional GLRT charts.

Original languageEnglish
Pages (from-to)842-856
Number of pages15
JournalEnergy
Volume159
DOIs
Publication statusPublished - 15 Sep 2018

Fingerprint

Fault detection
Monitoring
Statistics

Keywords

  • Failure detection (FD)
  • Generalized likelihood ratio test (GLRT)
  • Multiscale
  • Photovoltaic (PV) systems
  • Weighted GLRT (WGLRT)

ASJC Scopus subject areas

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

Cite this

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title = "An effective statistical fault detection technique for grid connected photovoltaic systems based on an improved generalized likelihood ratio test",
abstract = "This paper proposes an improved statistical failure detection technique for enhanced monitoring capabilities of PV systems. The proposed technique offers reduced false alarm and missed detection rates compared to the generalized likelihood ratio test (GLRT) by taking into consideration the nature variance of the GLRT statistics and applying a multiscale representation. The multiscale nature of the data provides better robustness to noises and better monitoring quality. The effectiveness of the proposed multiscale weighted GLRT (MS-WGLRT) method in detecting failures is evaluated using a set of synthetic and simulated PV data where the developed chart is used for detecting single and multiple failures (e.g., Bypass, Mix and Shading failures). Moreover, a set of real-data was used in order to prove the effectiveness of the proposed technique in detecting partial shading faults. All results show that the MS-WGLRT method offers better fault detection performances compared to the classical WGLRT and conventional GLRT charts.",
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author = "Majdi Mansouri and Mansour Hajji and Mohamed Trabelsi and Mohamed-Faouzi Harkat and Ayman Al-khazraji and Andreas Livera and Hazem Nounou and Mohamed Nounou",
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AU - Mansouri, Majdi

AU - Hajji, Mansour

AU - Trabelsi, Mohamed

AU - Harkat, Mohamed-Faouzi

AU - Al-khazraji, Ayman

AU - Livera, Andreas

AU - Nounou, Hazem

AU - Nounou, Mohamed

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N2 - This paper proposes an improved statistical failure detection technique for enhanced monitoring capabilities of PV systems. The proposed technique offers reduced false alarm and missed detection rates compared to the generalized likelihood ratio test (GLRT) by taking into consideration the nature variance of the GLRT statistics and applying a multiscale representation. The multiscale nature of the data provides better robustness to noises and better monitoring quality. The effectiveness of the proposed multiscale weighted GLRT (MS-WGLRT) method in detecting failures is evaluated using a set of synthetic and simulated PV data where the developed chart is used for detecting single and multiple failures (e.g., Bypass, Mix and Shading failures). Moreover, a set of real-data was used in order to prove the effectiveness of the proposed technique in detecting partial shading faults. All results show that the MS-WGLRT method offers better fault detection performances compared to the classical WGLRT and conventional GLRT charts.

AB - This paper proposes an improved statistical failure detection technique for enhanced monitoring capabilities of PV systems. The proposed technique offers reduced false alarm and missed detection rates compared to the generalized likelihood ratio test (GLRT) by taking into consideration the nature variance of the GLRT statistics and applying a multiscale representation. The multiscale nature of the data provides better robustness to noises and better monitoring quality. The effectiveness of the proposed multiscale weighted GLRT (MS-WGLRT) method in detecting failures is evaluated using a set of synthetic and simulated PV data where the developed chart is used for detecting single and multiple failures (e.g., Bypass, Mix and Shading failures). Moreover, a set of real-data was used in order to prove the effectiveness of the proposed technique in detecting partial shading faults. All results show that the MS-WGLRT method offers better fault detection performances compared to the classical WGLRT and conventional GLRT charts.

KW - Failure detection (FD)

KW - Generalized likelihood ratio test (GLRT)

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KW - Photovoltaic (PV) systems

KW - Weighted GLRT (WGLRT)

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