Construction of a Fast Monitoring System for Electric Energy Equipment Status Based on Data Mining
In modern power system operation, it is crucial to achieve fast and accurate monitoring of the electrical equipment status. To achieve this fast and accurate detection, this study proposes a generative adversarial network that combines edge features to amplify and recognize infrared images of device...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
European Alliance for Innovation (EAI)
2024-12-01
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| Series: | EAI Endorsed Transactions on Energy Web |
| Subjects: | |
| Online Access: | https://publications.eai.eu/index.php/ew/article/view/5869 |
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| _version_ | 1850053466499579904 |
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| author | Fusheng Wei Xue Li Weiwen Chen Zhaokai Liang Zhaopeng Huang |
| author_facet | Fusheng Wei Xue Li Weiwen Chen Zhaokai Liang Zhaopeng Huang |
| author_sort | Fusheng Wei |
| collection | DOAJ |
| description | In modern power system operation, it is crucial to achieve fast and accurate monitoring of the electrical equipment status. To achieve this fast and accurate detection, this study proposes a generative adversarial network that combines edge features to amplify and recognize infrared images of devices, aiming to improve the model’s training effect. This model extracted edge features from infrared images to eliminate background noise in infrared images to achieve the goal of improving the accurate monitoring of the status of electrical equipment. The results showed that on the balanced dataset, the recognition accuracy of the model could reach about 96%, and the recognition effect of the model was relatively stable. On imbalanced datasets, the highest model recognition accuracy was around 89%, and the model recognition accuracy fluctuated greatly. The constructed model effectively improves the accuracy of monitoring the operating status of electric energy equipment, achieving fast and accurate monitoring of this state. This study can achieve rapid monitoring of the operating status of electric energy equipment, effectively reducing the operation and maintenance costs of the power system.
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| format | Article |
| id | doaj-art-38cd6435471a4dd7aec417ced5fcda79 |
| institution | DOAJ |
| issn | 2032-944X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Energy Web |
| spelling | doaj-art-38cd6435471a4dd7aec417ced5fcda792025-08-20T02:52:31ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Energy Web2032-944X2024-12-011210.4108/ew.5869Construction of a Fast Monitoring System for Electric Energy Equipment Status Based on Data MiningFusheng Wei0Xue Li1Weiwen Chen2Zhaokai Liang3Zhaopeng Huang4Guandong Power Grid Co.Guandong Power Grid Co.Guandong Power Grid Co.Guangzhou Power Supply Bureau of Guangdong Power Grid Co.Foshan Power Supply Bureau of Guangdong Power Grid Co.In modern power system operation, it is crucial to achieve fast and accurate monitoring of the electrical equipment status. To achieve this fast and accurate detection, this study proposes a generative adversarial network that combines edge features to amplify and recognize infrared images of devices, aiming to improve the model’s training effect. This model extracted edge features from infrared images to eliminate background noise in infrared images to achieve the goal of improving the accurate monitoring of the status of electrical equipment. The results showed that on the balanced dataset, the recognition accuracy of the model could reach about 96%, and the recognition effect of the model was relatively stable. On imbalanced datasets, the highest model recognition accuracy was around 89%, and the model recognition accuracy fluctuated greatly. The constructed model effectively improves the accuracy of monitoring the operating status of electric energy equipment, achieving fast and accurate monitoring of this state. This study can achieve rapid monitoring of the operating status of electric energy equipment, effectively reducing the operation and maintenance costs of the power system. https://publications.eai.eu/index.php/ew/article/view/5869Data miningElectric energy equipmentStatus monitoringEdge perceptionGenerative adversarial network |
| spellingShingle | Fusheng Wei Xue Li Weiwen Chen Zhaokai Liang Zhaopeng Huang Construction of a Fast Monitoring System for Electric Energy Equipment Status Based on Data Mining EAI Endorsed Transactions on Energy Web Data mining Electric energy equipment Status monitoring Edge perception Generative adversarial network |
| title | Construction of a Fast Monitoring System for Electric Energy Equipment Status Based on Data Mining |
| title_full | Construction of a Fast Monitoring System for Electric Energy Equipment Status Based on Data Mining |
| title_fullStr | Construction of a Fast Monitoring System for Electric Energy Equipment Status Based on Data Mining |
| title_full_unstemmed | Construction of a Fast Monitoring System for Electric Energy Equipment Status Based on Data Mining |
| title_short | Construction of a Fast Monitoring System for Electric Energy Equipment Status Based on Data Mining |
| title_sort | construction of a fast monitoring system for electric energy equipment status based on data mining |
| topic | Data mining Electric energy equipment Status monitoring Edge perception Generative adversarial network |
| url | https://publications.eai.eu/index.php/ew/article/view/5869 |
| work_keys_str_mv | AT fushengwei constructionofafastmonitoringsystemforelectricenergyequipmentstatusbasedondatamining AT xueli constructionofafastmonitoringsystemforelectricenergyequipmentstatusbasedondatamining AT weiwenchen constructionofafastmonitoringsystemforelectricenergyequipmentstatusbasedondatamining AT zhaokailiang constructionofafastmonitoringsystemforelectricenergyequipmentstatusbasedondatamining AT zhaopenghuang constructionofafastmonitoringsystemforelectricenergyequipmentstatusbasedondatamining |