An Internal Overvoltage Identification Method for Distribution Network Based on Transfer Learning
As a measure for internal overvoltage identification of distribution network, the data driving method is limited in practical applications due to the small number of overvoltage samples. A transfer-learning-based deep convolutional neural network (D-CNN) algorithm is thus proposed to identify the in...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
State Grid Energy Research Institute
2021-08-01
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| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202006274 |
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| Summary: | As a measure for internal overvoltage identification of distribution network, the data driving method is limited in practical applications due to the small number of overvoltage samples. A transfer-learning-based deep convolutional neural network (D-CNN) algorithm is thus proposed to identify the internal overvoltage of distribution network. Firstly, 6 types of two-dimension time-frequency maps of 10 kV distribution network internal overvoltage are constructed by simulation and continuous wavelet transform (CWT). Then, the transfer-learning-based D-CNN network models are built using four network models, including Alexnet, Vgg-16, Googlenet and Resnet50. Finally, the two-dimension time-frequency maps are introduced into the transformed D-CNN for training. By comparing and analyzing the experimental results, it is found that the newly constructed VGG-16 network model has the highest identification accuracy, reaching 99.07%, which realizes the accurate classification of overvoltage faults in the case of scarce data. |
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| ISSN: | 1004-9649 |