An Effective Evaluation on Fault Detection in Solar Panels

Joshuva Arockia Dhanraj; Ali Mostafaeipour; Karthikeyan Velmurugan; Kuaanan Techato; Prem Kumar Chaurasiya; Jenoris Muthiya Solomon; Anitha Gopalan; Khamphe Phoungthong

LAPSE

Living Archive for Process Systems Engineering

LAPSE:2023.17795

Published Article

LAPSE:2023.17795

An Effective Evaluation on Fault Detection in Solar Panels

Joshuva Arockia Dhanraj, Ali Mostafaeipour, Karthikeyan Velmurugan, Kuaanan Techato, Prem Kumar Chaurasiya, Jenoris Muthiya Solomon, Anitha Gopalan, Khamphe Phoungthong

March 6, 2023

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.

Record ID

LAPSE:2023.17795

Keywords

Fault Detection, Machine Learning, power efficiency, solar panel

Subject

Process Control

Suggested Citation

Dhanraj JA, Mostafaeipour A, Velmurugan K, Techato K, Chaurasiya PK, Solomon JM, Gopalan A, Phoungthong K. An Effective Evaluation on Fault Detection in Solar Panels. (2023). LAPSE:2023.17795

Author Affiliations

Dhanraj JA: 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 Aut [ORCID]
Mostafaeipour A: 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 89158184
Velmurugan K: Environmental Assessment and Technology for Hazardous Waste Management Research Center, Faculty of Environmental Management, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand
Techato K: Environmental Assessment and Technology for Hazardous Waste Management Research Center, Faculty of Environmental Management, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand [ORCID]
Chaurasiya PK: Faculty of Mechanical Engineering Department, Sagar Institute of Science and Technology, Gandhi Nagar, Bhopal 462036, India
Solomon JM: Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka 560078, India
Gopalan A: 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
Phoungthong K: Environmental Assessment and Technology for Hazardous Waste Management Research Center, Faculty of Environmental Management, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand [ORCID]

Journal Name

Energies

Volume

14

Issue

22

First Page

7770

Year

2021

Publication Date

2021-11-19