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An Effective Evaluation on Fault Detection in Solar Panels

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  • Joshuva Arockia Dhanraj

    (Environmental Assessment and Technology for Hazardous Waste Management Research Center, Faculty of Environmental Management, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand
    Smart Green Grid Solutions, Chennai 600040, India
    Centre for Automation and Robotics (ANRO), Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Chennai 603103, India)

  • Ali Mostafaeipour

    (Environmental Assessment and Technology for Hazardous Waste Management Research Center, Faculty of Environmental Management, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand
    Industrial Engineering Department, Yazd University, Yazd 8915818411, Iran)

  • Karthikeyan Velmurugan

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

  • Kuaanan Techato

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

  • Prem Kumar Chaurasiya

    (Faculty of Mechanical Engineering Department, Sagar Institute of Science and Technology, Gandhi Nagar, Bhopal 462036, India)

  • Jenoris Muthiya Solomon

    (Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka 560078, India)

  • Anitha Gopalan

    (Department of Nano Electronics, Materials and Sensors, Institute of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India)

  • Khamphe Phoungthong

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

Abstract

The world’s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy sources, is a potential renewable resource to manage these enduring challenges in the energy crisis. Solar power generation is expanding globally as a result of growing energy demands and depleting fossil fuel reserves, which are presently the primary sources of power generation. In the realm of solar power generation, photovoltaic (PV) panels are used to convert solar radiation into energy. They are subjected to the constantly changing state of the environment, resulting in a wide range of defects. These defects should be discovered and remedied as soon as possible so that PV panels efficiency, endurance, and durability are not compromised. This paper focuses on five aspects, namely, (i) the various possible faults that occur in PV panels, (ii) the online/remote supervision of PV panels, (iii) the role of machine learning techniques in the fault diagnosis of PV panels, (iv) the various sensors used for different fault detections in PV panels, and (v) the benefits of fault identification in PV panels. Based on the investigated studies, recommendations for future research directions are suggested.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7770-:d:683266
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    References listed on IDEAS

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    5. Hee-Won Lim & Il-Kwon Kim & Ji-Hyeon Kim & U-Cheul Shin, 2022. "Simulation-Based Fault Detection Remote Monitoring System for Small-Scale Photovoltaic Systems," Energies, MDPI, vol. 15(24), pages 1-12, December.
    6. Nonthawat Khortsriwong & Promphak Boonraksa & Terapong Boonraksa & Thipwan Fangsuwannarak & Asada Boonsrirat & Watcharakorn Pinthurat & Boonruang Marungsri, 2023. "Performance of Deep Learning Techniques for Forecasting PV Power Generation: A Case Study on a 1.5 MWp Floating PV Power Plant," Energies, MDPI, vol. 16(5), pages 1-21, February.
    7. Abdulla, Hind & Sleptchenko, Andrei & Nayfeh, Ammar, 2024. "Photovoltaic systems operation and maintenance: A review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).

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