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Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems

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  • Livera, Andreas
  • Theristis, Marios
  • Makrides, George
  • Georghiou, George E.

Abstract

Over the last decade, research into photovoltaic (PV) technology has shifted from a race for the highest efficiency to the increase of the performance reliability in the field. A major part of current research activities focuses on the reliability of the installations and the guaranteed lifetime output through constant, solid and traceable PV plant monitoring. In this domain, several PV monitoring strategies for the early diagnosis of failures in grid-connected PV systems have been proposed in the literature and this study seeks to provide an overview of all the data analytic methods used by the research community and industry for the detection and classification of failures from acquired performance data of grid-connected PV systems. Insight into the performance monitoring requirements (parameters and resolution) for the detection of failures in monitored PV systems, as well as the various techniques used for their classification is also provided. Finally, this overview covers the data analytic methods based on electrical signature, numerical and statistical analysis and are summarised according to the type of failure, input requirements and validation procedure.

Suggested Citation

  • Livera, Andreas & Theristis, Marios & Makrides, George & Georghiou, George E., 2019. "Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 133(C), pages 126-143.
    • Handle: RePEc:eee:renene:v:133:y:2019:i:c:p:126-143
    DOI: 10.1016/j.renene.2018.09.101
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    14. Mahmudul Islam & Masud Rana Rashel & Md Tofael Ahmed & A. K. M. Kamrul Islam & Mouhaydine Tlemçani, 2023. "Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review," Energies, MDPI, vol. 16(21), pages 1-18, November.
    15. Francisco José Gimeno-Sales & Salvador Orts-Grau & Alejandro Escribá-Aparisi & Pablo González-Altozano & Ibán Balbastre-Peralta & Camilo Itzame Martínez-Márquez & María Gasque & Salvador Seguí-Chilet, 2020. "PV Monitoring System for a Water Pumping Scheme with a Lithium-Ion Battery Using Free Open-Source Software and IoT Technologies," Sustainability, MDPI, vol. 12(24), pages 1-28, December.
    16. Clavijo-Blanco, J.A. & Álvarez-Tey, G. & Saborido-Barba, N. & Barberá-González, J.L. & García-López, C. & Jiménez-Castañeda, R., 2021. "Laboratory tests for the evaluation of the degradation of a photovoltaic plant of 2.85 MWp with different classes of PV modules," Renewable Energy, Elsevier, vol. 174(C), pages 262-277.
    17. Qu, Jiaqi & Qian, Zheng & Pei, Yan & Wei, Lu & Zareipour, Hamidreza & Sun, Qiang, 2022. "An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection," Applied Energy, Elsevier, vol. 319(C).
    18. Van Gompel, Jonas & Spina, Domenico & Develder, Chris, 2022. "Satellite based fault diagnosis of photovoltaic systems using recurrent neural networks," Applied Energy, Elsevier, vol. 305(C).
    19. Duberney Murillo-Yarce & José Alarcón-Alarcón & Marco Rivera & Carlos Restrepo & Javier Muñoz & Carlos Baier & Patrick Wheeler, 2020. "A Review of Control Techniques in Photovoltaic Systems," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    20. Van Gompel, Jonas & Spina, Domenico & Develder, Chris, 2023. "Cost-effective fault diagnosis of nearby photovoltaic systems using graph neural networks," Energy, Elsevier, vol. 266(C).
    21. Belqasem Aljafari & Siva Rama Krishna Madeti & Priya Ranjan Satpathy & Sudhakar Babu Thanikanti & Bamidele Victor Ayodele, 2022. "Automatic Monitoring System for Online Module-Level Fault Detection in Grid-Tied Photovoltaic Plants," Energies, MDPI, vol. 15(20), pages 1-28, October.
    22. Tomáš Finsterle & Ladislava Černá & Pavel Hrzina & David Rokusek & Vítězslav Benda, 2021. "Diagnostics of PID Early Stage in PV Systems," Energies, MDPI, vol. 14(8), pages 1-15, April.
    23. 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).

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