Hotspot Detection of Solar Photovoltaic System: A Perspective from Image Processing

• Published in: 2023 IEEE 3rd International Conference in Power Engineering Applications: Shaping Sustainability Through Power Engineering Innovation, ICPEA 2023
• Main Author: Binti Ishak N.H.; Sazanita Binti Isa I.; Bin Osman M.K.; Daud K.; Bin Jadin M.S.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85156090331&doi=10.1109%2fICPEA56918.2023.10093148&partnerID=40&md5=41f0b5e45e928736a4763256a5164d13

Research in solar energy has rapidly grown since its significant and contributes to the advancement in clean renewable energy technology. Effective energy management such as fault detection impacts the early-stage monitoring for the efficiency, reliability, and safety of solar photovoltaic (PV) systems. The formation of a hotspot is one of the issues commonly occurred in a PV system. However, the main limitation of hotspot detection is the difficulty to interpret specific components with erratic temperatures in the thermographic images for attributes in the intelligence detection model. In this study, a review of hotspot detection in solar PV panels using the image processing method is established based on the image processing field. The integration of image processing approach can further assist in developing automated fault detection in solar PV farms for effective preventive monitoring methods. Therefore, several aspects need to be categorized and considered accordingly for achieving accurate prediction. Several ways were discussed, and future research is suggested in this study. © 2023 IEEE.