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

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

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.

Suggested Citation

  • Mansouri, Majdi & Hajji, Mansour & Trabelsi, Mohamed & Harkat, Mohamed Faouzi & Al-khazraji, Ayman & Livera, Andreas & Nounou, Hazem & Nounou, Mohamed, 2018. "An effective statistical fault detection technique for grid connected photovoltaic systems based on an improved generalized likelihood ratio test," Energy, Elsevier, vol. 159(C), pages 842-856.
    • Handle: RePEc:eee:energy:v:159:y:2018:i:c:p:842-856
    DOI: 10.1016/j.energy.2018.06.194
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    Cited by:

    1. Hussain, Muhammed & Dhimish, Mahmoud & Titarenko, Sofya & Mather, Peter, 2020. "Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters," Renewable Energy, Elsevier, vol. 155(C), pages 1272-1292.
    2. Rouani, Lahcene & Harkat, Mohamed Faouzi & Kouadri, Abdelmalek & Mekhilef, Saad, 2021. "Shading fault detection in a grid-connected PV system using vertices principal component analysis," Renewable Energy, Elsevier, vol. 164(C), pages 1527-1539.
    3. Tingting Pei & Xiaohong Hao, 2019. "A Fault Detection Method for Photovoltaic Systems Based on Voltage and Current Observation and Evaluation," Energies, MDPI, vol. 12(9), pages 1-16, May.
    4. Khadija Attouri & Majdi Mansouri & Mansour Hajji & Abdelmalek Kouadri & Kais Bouzrara & Hazem Nounou, 2023. "Wind Power Converter Fault Diagnosis Using Reduced Kernel PCA-Based BiLSTM," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    5. Tabar, Vahid Sohrabi & Ghassemzadeh, Saeid & Tohidi, Sajjad, 2021. "Increasing resiliency against information vulnerability of renewable resources in the operation of smart multi-area microgrid," Energy, Elsevier, vol. 220(C).
    6. Andreas Livera & Georgios Tziolis & Jose G. Franquelo & Ruben Gonzalez Bernal & George E. Georghiou, 2022. "Cloud-Based Platform for Photovoltaic Assets Diagnosis and Maintenance," Energies, MDPI, vol. 15(20), pages 1-25, October.
    7. Sufyan Samara & Emad Natsheh, 2020. "Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems," Sustainability, MDPI, vol. 12(5), pages 1-20, March.
    8. Bakdi, Azzeddine & Bounoua, Wahiba & Mekhilef, Saad & Halabi, Laith M., 2019. "Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV," Energy, Elsevier, vol. 189(C).
    9. Manel Marweni & Mansour Hajji & Majdi Mansouri & Mohamed Fouazi Mimouni, 2023. "Photovoltaic Power Forecasting Using Multiscale-Model-Based Machine Learning Techniques," Energies, MDPI, vol. 16(12), pages 1-16, June.
    10. Fezai, R. & Mansouri, M. & Trabelsi, M. & Hajji, M. & Nounou, H. & Nounou, M., 2019. "Online reduced kernel GLRT technique for improved fault detection in photovoltaic systems," Energy, Elsevier, vol. 179(C), pages 1133-1154.

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