Review of Deep Learning-Based Hotspot Detection in Solar Photovoltaic Arrays
This review paper conducts a detailed exploration of the burgeoning field that leverages deep learning techniques for hotspot detection in solar photovoltaic (PV) arrays. Hotspots represent a paramount concern within PV systems, as they not only result in efficiency degradation but also pose potenti...
| Published in: | 2024 IEEE 4th International Conference in Power Engineering Applications: Powering the Future: Innovations for Sustainable Development, ICPEA 2024 |
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| Main Author: | |
| Format: | Conference paper |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2024
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191698409&doi=10.1109%2fICPEA60617.2024.10498989&partnerID=40&md5=6c0de85cb6b48d86dfc618ad2da31f6f |
| Summary: | This review paper conducts a detailed exploration of the burgeoning field that leverages deep learning techniques for hotspot detection in solar photovoltaic (PV) arrays. Hotspots represent a paramount concern within PV systems, as they not only result in efficiency degradation but also pose potential safety risks. This analysis encompasses a wide array of deep learning models and methodologies employed for hotspot detection, providing a rigorous assessment of their performance metrics. Furthermore, we engage in a forward-looking discussion concerning the promising future prospects and emerging trends within this dynamic and evolving research domain. As the global demand for renewable energy sources continues to rise, effectively addressing hotspots becomes more crucial for ensuring the sustainability and efficiency of solar PV arrays. © 2024 IEEE. |
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| DOI: | 10.1109/ICPEA60617.2024.10498989 |