Artificial Intelligence for DC Arc Fault Detection in Photovoltaic Systems: A Comprehensive Review
Photovoltaic (PV) systems are increasingly used for renewable energy generation but remain vulnerable to series arc faults, which can cause serious safety risks, fire hazards, and system failures. Detecting these faults in DC circuits is challenging due to their subtle electrical signatures and the...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11009182/ |
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| author | Kamal Chandra Paul Disnebio Waldmann Chen Chen Yao Wang Tiefu Zhao |
| author_facet | Kamal Chandra Paul Disnebio Waldmann Chen Chen Yao Wang Tiefu Zhao |
| author_sort | Kamal Chandra Paul |
| collection | DOAJ |
| description | Photovoltaic (PV) systems are increasingly used for renewable energy generation but remain vulnerable to series arc faults, which can cause serious safety risks, fire hazards, and system failures. Detecting these faults in DC circuits is challenging due to their subtle electrical signatures and the presence of noise from system and environmental sources. Traditional detection methods often fall short in terms of accuracy, prompting growing interest in artificial intelligence (AI)-based solutions. This review article provides a comprehensive analysis of AI-based techniques for series arc fault detection in PV systems, covering key aspects such as data preprocessing, feature extraction, model optimization, and hardware implementation. It presents a structured comparison of existing methods, including their strengths and limitations, through descriptive discussion and summary tables. The review also includes a simplified flowchart to illustrate the typical AI-based DC arc fault detection process. Key challenges are discussed, along with future directions such as hybrid models, transfer learning, and implementation in resource-constrained edge devices. This work aims to support continued research and development by helping researchers and engineers better understand the strengths and limitations of current approaches and identify practical ways to improve arc fault detection for enhanced PV system safety and reliability. |
| format | Article |
| id | doaj-art-9e9435d5f93840fa82ee38a532eb8da7 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9e9435d5f93840fa82ee38a532eb8da72025-08-20T03:12:36ZengIEEEIEEE Access2169-35362025-01-0113907669079410.1109/ACCESS.2025.357252111009182Artificial Intelligence for DC Arc Fault Detection in Photovoltaic Systems: A Comprehensive ReviewKamal Chandra Paul0https://orcid.org/0000-0001-8510-6805Disnebio Waldmann1Chen Chen2https://orcid.org/0000-0003-3957-7061Yao Wang3https://orcid.org/0000-0001-7438-8800Tiefu Zhao4https://orcid.org/0000-0002-5548-8555Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, Charlotte, NC, USARohde & Schwarz, München, Bayern, GermanyCenter for Research in Computer Vision, University of Central Florida, Orlando, FL, USASchool of Electrical Engineering, Hebei University of Technology, Tianjin, ChinaDepartment of Electrical and Computer Engineering, University of North Carolina at Charlotte, Charlotte, NC, USAPhotovoltaic (PV) systems are increasingly used for renewable energy generation but remain vulnerable to series arc faults, which can cause serious safety risks, fire hazards, and system failures. Detecting these faults in DC circuits is challenging due to their subtle electrical signatures and the presence of noise from system and environmental sources. Traditional detection methods often fall short in terms of accuracy, prompting growing interest in artificial intelligence (AI)-based solutions. This review article provides a comprehensive analysis of AI-based techniques for series arc fault detection in PV systems, covering key aspects such as data preprocessing, feature extraction, model optimization, and hardware implementation. It presents a structured comparison of existing methods, including their strengths and limitations, through descriptive discussion and summary tables. The review also includes a simplified flowchart to illustrate the typical AI-based DC arc fault detection process. Key challenges are discussed, along with future directions such as hybrid models, transfer learning, and implementation in resource-constrained edge devices. This work aims to support continued research and development by helping researchers and engineers better understand the strengths and limitations of current approaches and identify practical ways to improve arc fault detection for enhanced PV system safety and reliability.https://ieeexplore.ieee.org/document/11009182/Arc dischargearc fault detectionartificial neural networkdeep learningconvolutional neural networkknowledge distillation |
| spellingShingle | Kamal Chandra Paul Disnebio Waldmann Chen Chen Yao Wang Tiefu Zhao Artificial Intelligence for DC Arc Fault Detection in Photovoltaic Systems: A Comprehensive Review IEEE Access Arc discharge arc fault detection artificial neural network deep learning convolutional neural network knowledge distillation |
| title | Artificial Intelligence for DC Arc Fault Detection in Photovoltaic Systems: A Comprehensive Review |
| title_full | Artificial Intelligence for DC Arc Fault Detection in Photovoltaic Systems: A Comprehensive Review |
| title_fullStr | Artificial Intelligence for DC Arc Fault Detection in Photovoltaic Systems: A Comprehensive Review |
| title_full_unstemmed | Artificial Intelligence for DC Arc Fault Detection in Photovoltaic Systems: A Comprehensive Review |
| title_short | Artificial Intelligence for DC Arc Fault Detection in Photovoltaic Systems: A Comprehensive Review |
| title_sort | artificial intelligence for dc arc fault detection in photovoltaic systems a comprehensive review |
| topic | Arc discharge arc fault detection artificial neural network deep learning convolutional neural network knowledge distillation |
| url | https://ieeexplore.ieee.org/document/11009182/ |
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