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Unified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line

Author

Listed:
  • Imran Hussain

    (Department of Electrical Engineering, University of Engineering and Technology, Taxila 47050, Pakistan)

  • Ihsan Ullah Khalil

    (College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Aqsa Islam

    (Department of Physics, University of Agriculture, Faisalabad 38000, Pakistan)

  • Mati Ullah Ahsan

    (Department of Electrical Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Batu Pahat 86400, Malaysia)

  • Taosif Iqbal

    (College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Md. Shahariar Chowdhury

    (Faculties Environmental Management, Prince of Songkla University, Songkhla 90110, Thailand
    Environmental Assessment and Technology for Hazardous Waste Management Research Centre, Faculty of Environmental Management, Prince of Songkla University, Songkhla 90110, Thailand)

  • Kuaanan Techato

    (Faculties Environmental Management, Prince of Songkla University, Songkhla 90110, Thailand
    Environmental Assessment and Technology for Hazardous Waste Management Research Centre, Faculty of Environmental Management, Prince of Songkla University, Songkhla 90110, Thailand)

  • Nasim Ullah

    (Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

Abstract

Solar photovoltaic PV plants worldwide are continuously monitored and carefully protected to ensure safe and reliable operation through detecting and isolating faults. Faults are very common in modern solar PV systems which interrupt normal system operation adversely affecting the performance of the PV systems. When undetected, faults not only cause significant reduction in the efficiency and life span of the PV system, but also result in damage and fire hazards compromising their reliability. Therefore, early fault detection and diagnosis of photovoltaic plants is a necessity for safe and reliable operation required for growing solar PV systems. Unfortunately, several recent fire incidents have been reported recently caused by undetected faults in solar PV systems. Motivated by this challenge, this paper, utilizing a proposed fuzzy logic algorithm, presents a novel technique for detecting and classifying faults in solar PV systems. Furthermore, the proposed method introduces fault indexing as a performance indicator that measures the degree of deviation from the normal operating conditions of the photovoltaic system. Various signatures of each fault scenario are identified in the shape of corresponding current-voltage trajectories and their extracted parameters. The effectiveness of the proposed technique is evaluated both in simulation and experimentally using a 5 kW grid connected solar array. It is demonstrated that the proposed technique is capable of diagnosing the occurrence of different faults with more than 98% accuracy.

Suggested Citation

  • Imran Hussain & Ihsan Ullah Khalil & Aqsa Islam & Mati Ullah Ahsan & Taosif Iqbal & Md. Shahariar Chowdhury & Kuaanan Techato & Nasim Ullah, 2022. "Unified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line," Energies, MDPI, vol. 15(14), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5106-:d:861636
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    References listed on IDEAS

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    1. Gomathy Balasubramani & Venkatesan Thangavelu & Muniraj Chinnusamy & Umashankar Subramaniam & Sanjeevikumar Padmanaban & Lucian Mihet-Popa, 2020. "Infrared Thermography Based Defects Testing of Solar Photovoltaic Panel with Fuzzy Rule-Based Evaluation," Energies, MDPI, vol. 13(6), pages 1-14, March.
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    Cited by:

    1. Tito G. Amaral & Vitor Fernão Pires & Armando Cordeiro & Daniel Foito & João F. Martins & Julia Yamnenko & Tetyana Tereschenko & Liudmyla Laikova & Ihor Fedin, 2023. "Incipient Fault Diagnosis of a Grid-Connected T-Type Multilevel Inverter Using Multilayer Perceptron and Walsh Transform," Energies, MDPI, vol. 16(6), pages 1-18, March.

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