A novel infrared thermography image analysis for transformer condition monitoring
Electrical systems are deeply ingrained in most industrial facilities, their maintenance is increasingly becoming a critical and significant component of economic policy. Condition monitoring of electrical transformers is essential for improving their dependability and availability, averting costly...
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Elsevier
2024-12-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671124003383 |
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| author | Rupali Balabantaraya Ashwin Kumar Sahoo Prabodh Kumar Sahoo Chayan Mondal Abir Manoj Kumar Panda |
| author_facet | Rupali Balabantaraya Ashwin Kumar Sahoo Prabodh Kumar Sahoo Chayan Mondal Abir Manoj Kumar Panda |
| author_sort | Rupali Balabantaraya |
| collection | DOAJ |
| description | Electrical systems are deeply ingrained in most industrial facilities, their maintenance is increasingly becoming a critical and significant component of economic policy. Condition monitoring of electrical transformers is essential for improving their dependability and availability, averting costly maintenance and additional significant breakdowns. This research adopts the approach based on infrared thermography techniques (IRT) to keep eye on electrical transformers and detect their defects. Thermal images of the transformer were captured at two distinct operating states with an infrared camera. These images were then compiled into a dataset for further analysis. This method uses infrared imaging (IRT), along with feature analysis and machine learning, to identify issues in electrical transformers in a new way. To find the best performing machine learning model, different techniques are compared here in terms of their accuracy and stability. Two approaches are investigated for identifying features in thermography images. Approach-1 employed five common machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Decision Tree (DT), Logistic Regression (LR), and Least Squares Support Vector Machine (LS-SVM). Approach-2 utilized four deep learning techniques, such as MobileNetV2 (MNV2), InceptionV3 (InV3), DenseNet121(DN121), and our proposed modified VGG-16. Among all evaluated methods, the modified VGG-16 architecture achieved the highest level of dependability, demonstrating exceptional efficiency and accuracy in transformer condition monitoring and fault diagnosis. |
| format | Article |
| id | doaj-art-770ca643069b463d8ae0800f2c3950ec |
| institution | OA Journals |
| issn | 2772-6711 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj-art-770ca643069b463d8ae0800f2c3950ec2025-08-20T02:35:47ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112024-12-011010075810.1016/j.prime.2024.100758A novel infrared thermography image analysis for transformer condition monitoringRupali Balabantaraya0Ashwin Kumar Sahoo1Prabodh Kumar Sahoo2Chayan Mondal Abir3Manoj Kumar Panda4Department of Electrical Engineering, C. V. Raman Global University, Bhubaneshwar, Odisha, IndiaDepartment of Electrical Engineering, C. V. Raman Global University, Bhubaneshwar, Odisha, IndiaDepartment of Mechatronics Engineering, Parul Institute of Technology, Parul University, Vadodara, Gujarat, India; Corresponding author at: Department of Mechatronics Engineering, Parul Institute of Technology, Parul University, Waghodia, Vadodara, Gujarat, India, 391760.Department of Electrical Engineering, C. V. Raman Global University, Bhubaneshwar, Odisha, IndiaDepartment of Electronics and Communication Engineering, GIET University, Gunupur, Odisha, IndiaElectrical systems are deeply ingrained in most industrial facilities, their maintenance is increasingly becoming a critical and significant component of economic policy. Condition monitoring of electrical transformers is essential for improving their dependability and availability, averting costly maintenance and additional significant breakdowns. This research adopts the approach based on infrared thermography techniques (IRT) to keep eye on electrical transformers and detect their defects. Thermal images of the transformer were captured at two distinct operating states with an infrared camera. These images were then compiled into a dataset for further analysis. This method uses infrared imaging (IRT), along with feature analysis and machine learning, to identify issues in electrical transformers in a new way. To find the best performing machine learning model, different techniques are compared here in terms of their accuracy and stability. Two approaches are investigated for identifying features in thermography images. Approach-1 employed five common machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Decision Tree (DT), Logistic Regression (LR), and Least Squares Support Vector Machine (LS-SVM). Approach-2 utilized four deep learning techniques, such as MobileNetV2 (MNV2), InceptionV3 (InV3), DenseNet121(DN121), and our proposed modified VGG-16. Among all evaluated methods, the modified VGG-16 architecture achieved the highest level of dependability, demonstrating exceptional efficiency and accuracy in transformer condition monitoring and fault diagnosis.http://www.sciencedirect.com/science/article/pii/S2772671124003383Infrared ImagesKNNDTDeep learningMobileNetV2VGG-16 |
| spellingShingle | Rupali Balabantaraya Ashwin Kumar Sahoo Prabodh Kumar Sahoo Chayan Mondal Abir Manoj Kumar Panda A novel infrared thermography image analysis for transformer condition monitoring e-Prime: Advances in Electrical Engineering, Electronics and Energy Infrared Images KNN DT Deep learning MobileNetV2 VGG-16 |
| title | A novel infrared thermography image analysis for transformer condition monitoring |
| title_full | A novel infrared thermography image analysis for transformer condition monitoring |
| title_fullStr | A novel infrared thermography image analysis for transformer condition monitoring |
| title_full_unstemmed | A novel infrared thermography image analysis for transformer condition monitoring |
| title_short | A novel infrared thermography image analysis for transformer condition monitoring |
| title_sort | novel infrared thermography image analysis for transformer condition monitoring |
| topic | Infrared Images KNN DT Deep learning MobileNetV2 VGG-16 |
| url | http://www.sciencedirect.com/science/article/pii/S2772671124003383 |
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