A Robust Multi-Modal Deep Learning-Based Fault Diagnosis Method for PV Systems
In this paper, a robust, multi-modal deep-learning-based fault identification method is proposed for solar photovoltaic (PV) systems, capable of detecting a wide range of faults at PV arrays, inverters, sensors, and grid connections. The proposed method combines residual convolutional neural network...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
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| Online Access: | https://ieeexplore.ieee.org/document/10752620/ |
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| author | Shahabodin Afrasiabi Sarah Allahmoradi Mousa Afrasiabi Xiaodong Liang C. Y. Chung Jamshid Aghaei |
| author_facet | Shahabodin Afrasiabi Sarah Allahmoradi Mousa Afrasiabi Xiaodong Liang C. Y. Chung Jamshid Aghaei |
| author_sort | Shahabodin Afrasiabi |
| collection | DOAJ |
| description | In this paper, a robust, multi-modal deep-learning-based fault identification method is proposed for solar photovoltaic (PV) systems, capable of detecting a wide range of faults at PV arrays, inverters, sensors, and grid connections. The proposed method combines residual convolutional neural networks (CNNs) and gated recurrent units (GRUs) to effectively extract both spatial and temporal features from raw PV data. To enhance the proposed model’s robustness and accuracy, a probabilistic loss function based on the entropy theory is formulated. The proposed method is validated using both experimental data obtained from a PV emulator-based test system and simulation data, achieving over 98% accuracy in fault identification under various noise conditions. The results indicate that the proposed model outperforms conventional CNN- and MSVM-based methods, demonstrating its potential in providing precise fault diagnostics in PV systems. |
| format | Article |
| id | doaj-art-0f76c271f0d2430ca59502036b07c2fe |
| institution | Kabale University |
| issn | 2687-7910 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj-art-0f76c271f0d2430ca59502036b07c2fe2025-01-21T00:03:12ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102024-01-011158359410.1109/OAJPE.2024.349788010752620A Robust Multi-Modal Deep Learning-Based Fault Diagnosis Method for PV SystemsShahabodin Afrasiabi0https://orcid.org/0000-0002-1452-3924Sarah Allahmoradi1Mousa Afrasiabi2Xiaodong Liang3https://orcid.org/0000-0002-8089-5419C. Y. Chung4https://orcid.org/0000-0001-6607-2240Jamshid Aghaei5https://orcid.org/0000-0002-5254-9148Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, CanadaDepartment of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, CanadaCyient, Vaasa, Ostrobothnia, FinlandDepartment of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, CanadaDepartment of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, ChinaSchool of Engineering and Technology, Central Queensland University, Rockhampton, QLD, AustraliaIn this paper, a robust, multi-modal deep-learning-based fault identification method is proposed for solar photovoltaic (PV) systems, capable of detecting a wide range of faults at PV arrays, inverters, sensors, and grid connections. The proposed method combines residual convolutional neural networks (CNNs) and gated recurrent units (GRUs) to effectively extract both spatial and temporal features from raw PV data. To enhance the proposed model’s robustness and accuracy, a probabilistic loss function based on the entropy theory is formulated. The proposed method is validated using both experimental data obtained from a PV emulator-based test system and simulation data, achieving over 98% accuracy in fault identification under various noise conditions. The results indicate that the proposed model outperforms conventional CNN- and MSVM-based methods, demonstrating its potential in providing precise fault diagnostics in PV systems.https://ieeexplore.ieee.org/document/10752620/Convolutional neural networks (CNNs)fault identificationfeature extractiongated neural networks (GNNs)information theoryloss function |
| spellingShingle | Shahabodin Afrasiabi Sarah Allahmoradi Mousa Afrasiabi Xiaodong Liang C. Y. Chung Jamshid Aghaei A Robust Multi-Modal Deep Learning-Based Fault Diagnosis Method for PV Systems IEEE Open Access Journal of Power and Energy Convolutional neural networks (CNNs) fault identification feature extraction gated neural networks (GNNs) information theory loss function |
| title | A Robust Multi-Modal Deep Learning-Based Fault Diagnosis Method for PV Systems |
| title_full | A Robust Multi-Modal Deep Learning-Based Fault Diagnosis Method for PV Systems |
| title_fullStr | A Robust Multi-Modal Deep Learning-Based Fault Diagnosis Method for PV Systems |
| title_full_unstemmed | A Robust Multi-Modal Deep Learning-Based Fault Diagnosis Method for PV Systems |
| title_short | A Robust Multi-Modal Deep Learning-Based Fault Diagnosis Method for PV Systems |
| title_sort | robust multi modal deep learning based fault diagnosis method for pv systems |
| topic | Convolutional neural networks (CNNs) fault identification feature extraction gated neural networks (GNNs) information theory loss function |
| url | https://ieeexplore.ieee.org/document/10752620/ |
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