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Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system

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  • Dhimish, Mahmoud
  • Holmes, Violeta
  • Mehrdadi, Bruce
  • Dales, Mark
  • Mather, Peter

Abstract

This work proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behavior of an existing PV system. For a given set of working conditions, solar irradiance and PV modules' temperature, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation (VI) LabVIEW software. Furthermore, a third order polynomial function is used to generate two detection limits for the VR and PR ratios obtained using VI LabVIEW simulation tool.

Suggested Citation

  • Dhimish, Mahmoud & Holmes, Violeta & Mehrdadi, Bruce & Dales, Mark & Mather, Peter, 2017. "Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system," Energy, Elsevier, vol. 140(P1), pages 276-290.
    • Handle: RePEc:eee:energy:v:140:y:2017:i:p1:p:276-290
    DOI: 10.1016/j.energy.2017.08.102
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    References listed on IDEAS

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    Cited by:

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    6. Joshuva Arockia Dhanraj & Ali Mostafaeipour & Karthikeyan Velmurugan & Kuaanan Techato & Prem Kumar Chaurasiya & Jenoris Muthiya Solomon & Anitha Gopalan & Khamphe Phoungthong, 2021. "An Effective Evaluation on Fault Detection in Solar Panels," Energies, MDPI, vol. 14(22), pages 1-14, November.
    7. Masoud Emamian & Aref Eskandari & Mohammadreza Aghaei & Amir Nedaei & Amirmohammad Moradi Sizkouhi & Jafar Milimonfared, 2022. "Cloud Computing and IoT Based Intelligent Monitoring System for Photovoltaic Plants Using Machine Learning Techniques," Energies, MDPI, vol. 15(9), pages 1-25, April.
    8. Sun, Chenhao & Wang, Xin & Zheng, Yihui, 2020. "An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks," Applied Energy, Elsevier, vol. 258(C).
    9. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    10. Mellit, Adel & Kalogirou, Soteris, 2022. "Assessment of machine learning and ensemble methods for fault diagnosis of photovoltaic systems," Renewable Energy, Elsevier, vol. 184(C), pages 1074-1090.
    11. Wu, Lijun & Chen, Zhicong & Long, Chao & Cheng, Shuying & Lin, Peijie & Chen, Yixiang & Chen, Huihuang, 2018. "Parameter extraction of photovoltaic models from measured I-V characteristics curves using a hybrid trust-region reflective algorithm," Applied Energy, Elsevier, vol. 232(C), pages 36-53.
    12. 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.
    13. Mojgan Hojabri & Samuel Kellerhals & Govinda Upadhyay & Benjamin Bowler, 2022. "IoT-Based PV Array Fault Detection and Classification Using Embedded Supervised Learning Methods," Energies, MDPI, vol. 15(6), pages 1-18, March.
    14. Ahmed A. Al-Katheri & Essam A. Al-Ammar & Majed A. Alotaibi & Wonsuk Ko & Sisam Park & Hyeong-Jin Choi, 2022. "Application of Artificial Intelligence in PV Fault Detection," Sustainability, MDPI, vol. 14(21), pages 1-25, October.
    15. Wang, Haizheng & Zhao, Jian & Sun, Qian & Zhu, Honglu, 2019. "Probability modeling for PV array output interval and its application in fault diagnosis," Energy, Elsevier, vol. 189(C).
    16. Tomasz Popławski & Marek Kurkowski & Jarosław Mirowski, 2020. "Improving the Quality of Electricity in Installations with Mixed Lighting Fittings," Energies, MDPI, vol. 13(22), pages 1-17, November.
    17. 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.

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