Journal
ENERGY REPORTS
Volume 8, Issue -, Pages 5898-5929Publisher
ELSEVIER
DOI: 10.1016/j.egyr.2022.04.043
Keywords
Classification; Detection; Electrical based method; Fault; Photovoltaic system; Visual and thermal method
Categories
Funding
- Ministry of Science and Technology, Taiwan [MOST 110-3116-F-008-001, 111-3116-F-008-001]
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Photovoltaic (PV) fault detection and classification are crucial for maintaining the reliability of PV systems. This paper improves the categorization of methods for studying faulty PV systems and provides specific techniques for fault detection and classification. It also suggests future research directions for emerging technologies.
Photovoltaic (PV) fault detection and classification are essential in maintaining the reliability of the PV system (PVS). Various faults may occur in either DC or AC side of the PVS. The detection, classification, and localization of such faults are essential for mitigation, accident prevention, reduction of the loss of generated energy, and revenue. In recent years, the number of works of PV fault detection and classification has significantly increased. These works have been reviewed by considering the categorization of detection and classification techniques. This paper improves of the categorization of methods to study the faulty PVS by considering visual and thermal method and electrical based method. Moreover, an effort is made to list all potential faults in a PVS in both the DC and AC sides. Specific PV fault detection and classification techniques are also enumerated. A possible direction for research on the PV fault detection and classification, such as quantum machine learning, internet of things, and cloud/edge computing technologies, is suggested as a guide for future emerging technologies. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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