Fault Detection of Smart Electricity Meters Based on 1D Convolution Twin Network
Timely detection and maintenance of smart electricity meter faults are essential for smart grid systems, but there is no high-accurate algorithm to detect the meter fault yet. So, in this paper, we propose a deep learning algorithm to detect the fault of the smart electricity meter. Our algorithm is...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2022-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/390884 |
| Summary: | Timely detection and maintenance of smart electricity meter faults are essential for smart grid systems, but there is no high-accurate algorithm to detect the meter fault yet. So, in this paper, we propose a deep learning algorithm to detect the fault of the smart electricity meter. Our algorithm is based on a 1D convolution twin network, which can distinguish the meter data of different fault types with high precision. To realize the fault detection task, we design a twin classifier for counting the number of matches between the data to be predicted and each type of known data and select the type with the most counts as the predicted type. Our algorithm automatically detects the fault of the smart electricity meter while its accuracy reaches 94.52%, which can significantly improve the maintenance efficiency of the fault detection. |
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| ISSN: | 1330-3651 1848-6339 |