A multi-scale dual-stage model for PV array fault detection, classification, and monitoring technique
The output generated by photovoltaic arrays is influenced mainly by the irradiance, which has non-uniform distribution in a day. This has resulted in the current-limiting nature and nonlinear output characteristics, and conventional protection devices cannot detect and clean faults appropriately. Th...
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| Format: | Article |
| Language: | English |
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Intelektual Pustaka Media Utama
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39245/ http://umpir.ump.edu.my/id/eprint/39245/1/A%20multi-scale%20dual-stage%20model%20for%20PV%20array%20fault%20detection.pdf |
| _version_ | 1848825718974185472 |
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| author | Siti Nor Azlina, Mohd Ghazali Muhamad Zahim, Sujod |
| author_facet | Siti Nor Azlina, Mohd Ghazali Muhamad Zahim, Sujod |
| author_sort | Siti Nor Azlina, Mohd Ghazali |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The output generated by photovoltaic arrays is influenced mainly by the irradiance, which has non-uniform distribution in a day. This has resulted in the current-limiting nature and nonlinear output characteristics, and conventional protection devices cannot detect and clean faults appropriately. This paper proposes a low-cost model for a multi-scale dual-stage photovoltaic fault detection, classification, and monitoring technique developed through MATLAB/Simulink. The main contribution of this paper is that it can detect multiple common faults, be applied on multi-scale photovoltaic arrays regardless of environmental conditions, and be beneficial for photovoltaic system maintenance work. The experimental results show that the developed algorithm using supervised learning algorithms mutual with k-fold cross-validation has produced good performances in identifying six common faults of photovoltaic arrays, achieved 100% accuracy in fault detection, and achieved good accuracy in fault classification. Challenges and suggestions for future research direction are also suggested in this paper. Overall, this study shall provide researchers and policymakers with a valuable reference for developing photovoltaic system fault detection and monitoring techniques for better feasibility, safety, and energy sustainability. |
| first_indexed | 2025-11-15T03:33:23Z |
| format | Article |
| id | ump-39245 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:33:23Z |
| publishDate | 2022 |
| publisher | Intelektual Pustaka Media Utama |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-392452023-11-09T01:17:45Z http://umpir.ump.edu.my/id/eprint/39245/ A multi-scale dual-stage model for PV array fault detection, classification, and monitoring technique Siti Nor Azlina, Mohd Ghazali Muhamad Zahim, Sujod TK Electrical engineering. Electronics Nuclear engineering The output generated by photovoltaic arrays is influenced mainly by the irradiance, which has non-uniform distribution in a day. This has resulted in the current-limiting nature and nonlinear output characteristics, and conventional protection devices cannot detect and clean faults appropriately. This paper proposes a low-cost model for a multi-scale dual-stage photovoltaic fault detection, classification, and monitoring technique developed through MATLAB/Simulink. The main contribution of this paper is that it can detect multiple common faults, be applied on multi-scale photovoltaic arrays regardless of environmental conditions, and be beneficial for photovoltaic system maintenance work. The experimental results show that the developed algorithm using supervised learning algorithms mutual with k-fold cross-validation has produced good performances in identifying six common faults of photovoltaic arrays, achieved 100% accuracy in fault detection, and achieved good accuracy in fault classification. Challenges and suggestions for future research direction are also suggested in this paper. Overall, this study shall provide researchers and policymakers with a valuable reference for developing photovoltaic system fault detection and monitoring techniques for better feasibility, safety, and energy sustainability. Intelektual Pustaka Media Utama 2022-06 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/39245/1/A%20multi-scale%20dual-stage%20model%20for%20PV%20array%20fault%20detection.pdf Siti Nor Azlina, Mohd Ghazali and Muhamad Zahim, Sujod (2022) A multi-scale dual-stage model for PV array fault detection, classification, and monitoring technique. International Journal of Applied Power Engineering, 11 (2). 134 -144. ISSN 2252-8792. (Published) http://doi.org/10.11591/ijape.v11.i2.pp134-144 http://doi.org/10.11591/ijape.v11.i2.pp134-144 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Siti Nor Azlina, Mohd Ghazali Muhamad Zahim, Sujod A multi-scale dual-stage model for PV array fault detection, classification, and monitoring technique |
| title | A multi-scale dual-stage model for PV array fault detection, classification, and monitoring technique |
| title_full | A multi-scale dual-stage model for PV array fault detection, classification, and monitoring technique |
| title_fullStr | A multi-scale dual-stage model for PV array fault detection, classification, and monitoring technique |
| title_full_unstemmed | A multi-scale dual-stage model for PV array fault detection, classification, and monitoring technique |
| title_short | A multi-scale dual-stage model for PV array fault detection, classification, and monitoring technique |
| title_sort | multi-scale dual-stage model for pv array fault detection, classification, and monitoring technique |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/39245/ http://umpir.ump.edu.my/id/eprint/39245/ http://umpir.ump.edu.my/id/eprint/39245/ http://umpir.ump.edu.my/id/eprint/39245/1/A%20multi-scale%20dual-stage%20model%20for%20PV%20array%20fault%20detection.pdf |