Fault prediction of smart meter based on spatio-temporal convolution neural network
The faults of smart meters are sudden, complex and multifaceted. A fault prediction method based on spatio-temporal convolutional neural network(ST-CNN) is proposed. Firstly, the sliding window is used to integrate the time information into the characteristic variables, and the input matrix with spa...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
National Computer System Engineering Research Institute of China
2022-03-01
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| Series: | Dianzi Jishu Yingyong |
| Subjects: | |
| Online Access: | http://www.chinaaet.com/article/3000147054 |
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| Summary: | The faults of smart meters are sudden, complex and multifaceted. A fault prediction method based on spatio-temporal convolutional neural network(ST-CNN) is proposed. Firstly, the sliding window is used to integrate the time information into the characteristic variables, and the input matrix with space-time characteristics is constructed. Then, combined with CNN, the fault prediction model of smart meter is established, and the model parameters are optimized by adaptive momentum estimation (Adam) algorithm. Finally, the actual field data are used to simulate the fault prediction model of smart meter based on ST-CNN. The results show that this method has high prediction accuracy and strong generalization ability. |
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| ISSN: | 0258-7998 |