Analysis of Fault Detection and Classification in Photovoltaic Arrays Using Neural Network-Based Methods
Photovoltaic (PV) systems are vital in the global renewable energy landscape because of their capability to harness solar energy efficiently. Ensuring the continuous and efficient operation of PV systems is crucial in maximizing their energy contribution. However, these systems' reliability and...
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| Format: | Article |
| Language: | English |
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Iran University of Science and Technology
2025-06-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
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| Online Access: | http://ijeee.iust.ac.ir/article-1-3617-en.pdf |
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| author | Arizadayana Zahalan Samila Mat Zali Ernie Che Mid Noor Fazliana Fadzail |
| author_facet | Arizadayana Zahalan Samila Mat Zali Ernie Che Mid Noor Fazliana Fadzail |
| author_sort | Arizadayana Zahalan |
| collection | DOAJ |
| description | Photovoltaic (PV) systems are vital in the global renewable energy landscape because of their capability to harness solar energy efficiently. Ensuring the continuous and efficient operation of PV systems is crucial in maximizing their energy contribution. However, these systems' reliability and safety remain critical because they are prone to various faults, mainly when operating in harsh environmental conditions. This study addresses these issues by exploring fault detection and classification in PV arrays using neural network (NN) -based techniques. A PV array model, consisting of 3x6 PV modules, was simulated using MATLAB Simulink to replicate real-world conditions and analyse various fault scenarios. An open circuit, a short circuit, and a degrading fault are the three types of faults considered in this study. The NN was trained on a dataset generated from the MATLAB Simulink model, encompassing normal operating and fault conditions. This training enables the network to learn the distinctive patterns associated with each fault type, enhancing its detection accuracy and classification capabilities. Simulation results demonstrate that the NN-based approach effectively identifies and classifies the three types of faults. |
| format | Article |
| id | doaj-art-37ffc22f5abc4734a0b2609caee0e9c2 |
| institution | OA Journals |
| issn | 1735-2827 2383-3890 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Iran University of Science and Technology |
| record_format | Article |
| series | Iranian Journal of Electrical and Electronic Engineering |
| spelling | doaj-art-37ffc22f5abc4734a0b2609caee0e9c22025-08-20T02:07:06ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902025-06-0121236173617Analysis of Fault Detection and Classification in Photovoltaic Arrays Using Neural Network-Based MethodsArizadayana Zahalan0Samila Mat Zali1Ernie Che Mid2Noor Fazliana Fadzail3 Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia Photovoltaic (PV) systems are vital in the global renewable energy landscape because of their capability to harness solar energy efficiently. Ensuring the continuous and efficient operation of PV systems is crucial in maximizing their energy contribution. However, these systems' reliability and safety remain critical because they are prone to various faults, mainly when operating in harsh environmental conditions. This study addresses these issues by exploring fault detection and classification in PV arrays using neural network (NN) -based techniques. A PV array model, consisting of 3x6 PV modules, was simulated using MATLAB Simulink to replicate real-world conditions and analyse various fault scenarios. An open circuit, a short circuit, and a degrading fault are the three types of faults considered in this study. The NN was trained on a dataset generated from the MATLAB Simulink model, encompassing normal operating and fault conditions. This training enables the network to learn the distinctive patterns associated with each fault type, enhancing its detection accuracy and classification capabilities. Simulation results demonstrate that the NN-based approach effectively identifies and classifies the three types of faults.http://ijeee.iust.ac.ir/article-1-3617-en.pdfphotovoltaic arraysfault detectionfault classificationneural network |
| spellingShingle | Arizadayana Zahalan Samila Mat Zali Ernie Che Mid Noor Fazliana Fadzail Analysis of Fault Detection and Classification in Photovoltaic Arrays Using Neural Network-Based Methods Iranian Journal of Electrical and Electronic Engineering photovoltaic arrays fault detection fault classification neural network |
| title | Analysis of Fault Detection and Classification in Photovoltaic Arrays Using Neural Network-Based Methods |
| title_full | Analysis of Fault Detection and Classification in Photovoltaic Arrays Using Neural Network-Based Methods |
| title_fullStr | Analysis of Fault Detection and Classification in Photovoltaic Arrays Using Neural Network-Based Methods |
| title_full_unstemmed | Analysis of Fault Detection and Classification in Photovoltaic Arrays Using Neural Network-Based Methods |
| title_short | Analysis of Fault Detection and Classification in Photovoltaic Arrays Using Neural Network-Based Methods |
| title_sort | analysis of fault detection and classification in photovoltaic arrays using neural network based methods |
| topic | photovoltaic arrays fault detection fault classification neural network |
| url | http://ijeee.iust.ac.ir/article-1-3617-en.pdf |
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