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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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2021-08-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202006274 |
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| author | Hao XU Liqiang LIU Chao LV |
| author_facet | Hao XU Liqiang LIU Chao LV |
| author_sort | Hao XU |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e28df35effb2493bbd5a4ced8d3e38c2 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2021-08-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-e28df35effb2493bbd5a4ced8d3e38c22025-08-20T02:59:19ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492021-08-01548525910.11930/j.issn.1004-9649.202006274zgdl-54-8-xuhaoAn Internal Overvoltage Identification Method for Distribution Network Based on Transfer LearningHao XU0Liqiang LIU1Chao LV2College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, ChinaCollege of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, ChinaInner Mongolia Electric Power Research Institute, Hohhot 010020, ChinaAs 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.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202006274internal overvoltage of distribution networkcontinuous wavelet transformtransfer learningdeep convolutional neural networkpattern recognition |
| spellingShingle | Hao XU Liqiang LIU Chao LV An Internal Overvoltage Identification Method for Distribution Network Based on Transfer Learning Zhongguo dianli internal overvoltage of distribution network continuous wavelet transform transfer learning deep convolutional neural network pattern recognition |
| title | An Internal Overvoltage Identification Method for Distribution Network Based on Transfer Learning |
| title_full | An Internal Overvoltage Identification Method for Distribution Network Based on Transfer Learning |
| title_fullStr | An Internal Overvoltage Identification Method for Distribution Network Based on Transfer Learning |
| title_full_unstemmed | An Internal Overvoltage Identification Method for Distribution Network Based on Transfer Learning |
| title_short | An Internal Overvoltage Identification Method for Distribution Network Based on Transfer Learning |
| title_sort | internal overvoltage identification method for distribution network based on transfer learning |
| topic | internal overvoltage of distribution network continuous wavelet transform transfer learning deep convolutional neural network pattern recognition |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202006274 |
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