Siti Nor Azlina, Mohd Ghazali and Muhamad Zahim, Sujod (2023) A comparative analysis of solar photovoltaic advanced fault detection and monitoring techniques. Electrica, 23 (1). pp. 137-148. ISSN 2619-9831. (Published)
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Abstract
The non-linear I-V characteristics of the photovoltaic output have affected fault detection methods to work accurately. This scenario can cause hidden faults in the system and reduces overall productivity. Fault detection and monitoring techniques are evolving in photovoltaic fault management systems. Until recently, model-based technique, output signal analysis technique, statistically based technique, and machine learning techniques are the four main advanced fault detection methods that researchers have widely studied. This study has identified the limitations and advantages of previous photovoltaic fault detection and monitoring techniques, especially their applicability to all sizes of photovoltaic systems. This study proposes a multi-scale dual-stage photovoltaic fault detection and monitoring technique for better system safety, efficiency, and reliability. Challenges and suggestions for future research directions are also provided in this study. Overall, this study shall provide researchers and policymakers with a valuable reference for developing better fault detection and monitoring techniques for photovoltaic systems.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Advanced fault detection and monitoring techniques; Machine learning techniques; Model-based technique; Output signal analysis technique; Photovoltaic; Statistically based technique |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies College of Engineering Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 05 Sep 2023 03:07 |
| Last Modified: | 05 Sep 2023 03:07 |
| URI: | http://umpir.ump.edu.my/id/eprint/38204 |
| Download Statistic: | View Download Statistics |
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