Image-Based Partial Discharge Identification in High Voltage Cables Using Hybrid Deep Network
Deep learning and digital image technologies have combined to create a potentially effective tool for identifying partial discharge (PD) patterns precisely. However, it is necessary to investigate which algorithm guarantees the best performance. The more common tools are restricted by a lack of trai...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10129861/ |
| _version_ | 1797814549279145984 |
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| author | Obaid Aldosari Mohammed A. Aldowsari Salem Mohammed Batiyah N. Kanagaraj |
| author_facet | Obaid Aldosari Mohammed A. Aldowsari Salem Mohammed Batiyah N. Kanagaraj |
| author_sort | Obaid Aldosari |
| collection | DOAJ |
| description | Deep learning and digital image technologies have combined to create a potentially effective tool for identifying partial discharge (PD) patterns precisely. However, it is necessary to investigate which algorithm guarantees the best performance. The more common tools are restricted by a lack of training data and an advanced model in itself. Therefore, the main goal of this paper is to develop an efficient hybrid network comprising two deep networks, long short-term memory (LSTM), and convolutional neural network (CNN), for identifying the form of PD. A total of <inline-formula> <tex-math notation="LaTeX">$8186\times 25$ </tex-math></inline-formula> (non-PD<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>PD) images were applied to assess the proposed methods. The size of the PD type was increased to 3675 images using data augmentation techniques. The results indicated that the integration of CNN and LSTM networks can provide a more robust implementation for PD detection. The integrated CNN-LSTM deep network based on data augmentation outperformed features derived from a single deep network. The recall, F-measure, and classification precision have 99.9% as a validation accuracy with a 99.8% intersection over union and a loss of 0.004. |
| first_indexed | 2024-03-13T08:09:17Z |
| format | Article |
| id | doaj.art-50cdc60aa7d0493ea7ebc558e0e13289 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T08:09:17Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-50cdc60aa7d0493ea7ebc558e0e132892023-05-31T23:00:36ZengIEEEIEEE Access2169-35362023-01-0111503255033310.1109/ACCESS.2023.327805410129861Image-Based Partial Discharge Identification in High Voltage Cables Using Hybrid Deep NetworkObaid Aldosari0https://orcid.org/0000-0001-9447-5565Mohammed A. Aldowsari1Salem Mohammed Batiyah2https://orcid.org/0000-0002-5832-2323N. Kanagaraj3Department of Electrical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Ad-Dawasir, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, King Khaled University, Abha, Saudi ArabiaDepartment of Electrical and Electronics Engineering Technology, Yanbu Industrial College, Yanbu Industrial, Al Madinah Al Munawwarah, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Ad-Dawasir, Saudi ArabiaDeep learning and digital image technologies have combined to create a potentially effective tool for identifying partial discharge (PD) patterns precisely. However, it is necessary to investigate which algorithm guarantees the best performance. The more common tools are restricted by a lack of training data and an advanced model in itself. Therefore, the main goal of this paper is to develop an efficient hybrid network comprising two deep networks, long short-term memory (LSTM), and convolutional neural network (CNN), for identifying the form of PD. A total of <inline-formula> <tex-math notation="LaTeX">$8186\times 25$ </tex-math></inline-formula> (non-PD<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>PD) images were applied to assess the proposed methods. The size of the PD type was increased to 3675 images using data augmentation techniques. The results indicated that the integration of CNN and LSTM networks can provide a more robust implementation for PD detection. The integrated CNN-LSTM deep network based on data augmentation outperformed features derived from a single deep network. The recall, F-measure, and classification precision have 99.9% as a validation accuracy with a 99.8% intersection over union and a loss of 0.004.https://ieeexplore.ieee.org/document/10129861/Partial dischargeRGBgraydata augmentationLSTMCNN |
| spellingShingle | Obaid Aldosari Mohammed A. Aldowsari Salem Mohammed Batiyah N. Kanagaraj Image-Based Partial Discharge Identification in High Voltage Cables Using Hybrid Deep Network IEEE Access Partial discharge RGB gray data augmentation LSTM CNN |
| title | Image-Based Partial Discharge Identification in High Voltage Cables Using Hybrid Deep Network |
| title_full | Image-Based Partial Discharge Identification in High Voltage Cables Using Hybrid Deep Network |
| title_fullStr | Image-Based Partial Discharge Identification in High Voltage Cables Using Hybrid Deep Network |
| title_full_unstemmed | Image-Based Partial Discharge Identification in High Voltage Cables Using Hybrid Deep Network |
| title_short | Image-Based Partial Discharge Identification in High Voltage Cables Using Hybrid Deep Network |
| title_sort | image based partial discharge identification in high voltage cables using hybrid deep network |
| topic | Partial discharge RGB gray data augmentation LSTM CNN |
| url | https://ieeexplore.ieee.org/document/10129861/ |
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