2 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
JournalInternational Transactions on Electrical Energy Systems
DOIs
Publication statusAccepted/In press - 1 Jan 2018

Fingerprint

Generalized Likelihood Ratio Test
Failure Detection
Photovoltaic System
Chart
Feature extraction
Representation of data
Shading
Likelihood Ratio
False Alarm
Feature Extraction
Data analysis
Simulation

Keywords

  • Failure detection (FD)
  • Generalized likelihood ratio test (GLRT)
  • Multiscale representation
  • PV system
  • Weighted GLRT (WGLRT)

ASJC Scopus subject areas

  • Modelling and Simulation
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

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title = "Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems",
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.",
keywords = "Failure detection (FD), Generalized likelihood ratio test (GLRT), Multiscale representation, PV system, Weighted GLRT (WGLRT)",
author = "Majdi Mansouri and Mansour Hajji and Mohamed Trabelsi and Ayman Al-khazraji and Mohamed-Faouzi Harkat and Hazem Nounou and Mohamed Nounou",
year = "2018",
month = "1",
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language = "English",
journal = "International Transactions on Electrical Energy Systems",
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publisher = "John Wiley and Sons Ltd",

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T1 - Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems

AU - Mansouri, Majdi

AU - Hajji, Mansour

AU - Trabelsi, Mohamed

AU - Al-khazraji, Ayman

AU - Harkat, Mohamed-Faouzi

AU - Nounou, Hazem

AU - Nounou, Mohamed

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - Failure detection (FD)

KW - Generalized likelihood ratio test (GLRT)

KW - Multiscale representation

KW - PV system

KW - Weighted GLRT (WGLRT)

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JO - International Transactions on Electrical Energy Systems

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