Electrical Infrastructure Monitoring: Case of NTDCL’s 500kV Network Insulator Detection With YoloV8
High voltage electrical infrastructure inspection requires condition monitoring of transmission line assets to avoid any possible failures or emergency. Detection of insulators in strings is linked with electrical infrastructure monitoring pertaining to the insulator fault classification. The datase...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
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| Online Access: | https://ieeexplore.ieee.org/document/11096616/ |
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| author | Shafi Muhammad Jiskani Tanweer Hussain Anwar Ali Sahito Faheemullah Shaikh Laveet Kumar |
| author_facet | Shafi Muhammad Jiskani Tanweer Hussain Anwar Ali Sahito Faheemullah Shaikh Laveet Kumar |
| author_sort | Shafi Muhammad Jiskani |
| collection | DOAJ |
| description | High voltage electrical infrastructure inspection requires condition monitoring of transmission line assets to avoid any possible failures or emergency. Detection of insulators in strings is linked with electrical infrastructure monitoring pertaining to the insulator fault classification. The dataset widely available for insulator monitoring are either synthetic, lab created or publicly not available. In this paper, an indigenous dataset is created using Autonomous Aerial Vehicles (AAV) technology, capturing images in diverse topographical ambience across different transmission lines/circuits managed by National transmission and dispatch company ltd. in Pakistan. For detection of insulators in string, object detector model You Only Look Once-version 8 (YOLOv8n) is trained on created dataset of 3618 images, 603 being original and other augmented, after preprocessing and augmentation techniques were applied. The model’s performance is up to the mark with accuracy of 92%. The precision and recall being 0.95 and 0.90 respectively, whereas F1 score of the model peaked at 0.95 at confidence level of 0.652. |
| format | Article |
| id | doaj-art-8990b09e9d0c45ce99c87c9015b044ae |
| institution | Kabale University |
| issn | 2687-7910 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj-art-8990b09e9d0c45ce99c87c9015b044ae2025-08-20T03:43:52ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102025-01-011250551410.1109/OAJPE.2025.359269811096616Electrical Infrastructure Monitoring: Case of NTDCL’s 500kV Network Insulator Detection With YoloV8Shafi Muhammad Jiskani0Tanweer Hussain1https://orcid.org/0000-0002-6208-1702Anwar Ali Sahito2Faheemullah Shaikh3https://orcid.org/0000-0003-4469-828XLaveet Kumar4https://orcid.org/0000-0001-6932-1695Directorate of Postgraduate Studies, Mehran University of Engineering and Technology, Jamshoro, PakistanDepartment of Mechanical Engineering, Mehran University of Engineering and Technology, Jamshoro, PakistanDepartment of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro, PakistanDepartment of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro, PakistanDepartment of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha, QatarHigh voltage electrical infrastructure inspection requires condition monitoring of transmission line assets to avoid any possible failures or emergency. Detection of insulators in strings is linked with electrical infrastructure monitoring pertaining to the insulator fault classification. The dataset widely available for insulator monitoring are either synthetic, lab created or publicly not available. In this paper, an indigenous dataset is created using Autonomous Aerial Vehicles (AAV) technology, capturing images in diverse topographical ambience across different transmission lines/circuits managed by National transmission and dispatch company ltd. in Pakistan. For detection of insulators in string, object detector model You Only Look Once-version 8 (YOLOv8n) is trained on created dataset of 3618 images, 603 being original and other augmented, after preprocessing and augmentation techniques were applied. The model’s performance is up to the mark with accuracy of 92%. The precision and recall being 0.95 and 0.90 respectively, whereas F1 score of the model peaked at 0.95 at confidence level of 0.652.https://ieeexplore.ieee.org/document/11096616/Condition monitoringinsulator detectionindigenous datasetYou Only Look Once (YOLO)NTDCL Pakistan |
| spellingShingle | Shafi Muhammad Jiskani Tanweer Hussain Anwar Ali Sahito Faheemullah Shaikh Laveet Kumar Electrical Infrastructure Monitoring: Case of NTDCL’s 500kV Network Insulator Detection With YoloV8 IEEE Open Access Journal of Power and Energy Condition monitoring insulator detection indigenous dataset You Only Look Once (YOLO) NTDCL Pakistan |
| title | Electrical Infrastructure Monitoring: Case of NTDCL’s 500kV Network Insulator Detection With YoloV8 |
| title_full | Electrical Infrastructure Monitoring: Case of NTDCL’s 500kV Network Insulator Detection With YoloV8 |
| title_fullStr | Electrical Infrastructure Monitoring: Case of NTDCL’s 500kV Network Insulator Detection With YoloV8 |
| title_full_unstemmed | Electrical Infrastructure Monitoring: Case of NTDCL’s 500kV Network Insulator Detection With YoloV8 |
| title_short | Electrical Infrastructure Monitoring: Case of NTDCL’s 500kV Network Insulator Detection With YoloV8 |
| title_sort | electrical infrastructure monitoring case of ntdcl x2019 s 500kv network insulator detection with yolov8 |
| topic | Condition monitoring insulator detection indigenous dataset You Only Look Once (YOLO) NTDCL Pakistan |
| url | https://ieeexplore.ieee.org/document/11096616/ |
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