Fault Classification of Transmission Line Components Based on the Adversarial Continual Learning Model

ZHAO Zhenbing; JIANG Zhigang; XIONG Jing; NIE Liqiang; LÜ Xuechun

doi:10.11999/JEIT220200

Volume 44 Issue 11

Nov. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(11): 3757-3766

ZHAO Zhenbing, JIANG Zhigang, XIONG Jing, NIE Liqiang, LÜ Xuechun. Fault Classification of Transmission Line Components Based on the Adversarial Continual Learning Model[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3757-3766. doi: 10.11999/JEIT220200

Citation:

ZHAO Zhenbing, JIANG Zhigang, XIONG Jing, NIE Liqiang, LÜ Xuechun. Fault Classification of Transmission Line Components Based on the Adversarial Continual Learning Model[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3757-3766. doi: 10.11999/JEIT220200

Citation:

ZHAO Zhenbing, JIANG Zhigang, XIONG Jing, NIE Liqiang, LÜ Xuechun. Fault Classification of Transmission Line Components Based on the Adversarial Continual Learning Model[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3757-3766. doi: 10.11999/JEIT220200

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Fault Classification of Transmission Line Components Based on the Adversarial Continual Learning Model

doi: 10.11999/JEIT220200

1.
Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China
2.
Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, China
3.
Engineering Research Center of Intelligent Computing for Complex Energy Systems, Ministry of Education, Baoding 071003, China
4.
School of Computer Science and Technology, Shandong University, Qingdao 266237, China

Funds: The National Natural Science Foundation of China (61871182, U21A20486), The Natural Science Foundation of Hebei Province (F2020502009, F2021502008, F2021502013)

Received Date: 2022-03-01
Accepted Date: 2022-06-08
Rev Recd Date: 2022-05-26

Available Online: 2022-06-13

Publish Date: 2022-11-14

Abstract

Abstract

The inspection of transmission line fittings is an indispensable part of power grid security situation awareness. Focusing on the fact that the current transmission line component defect classification model cannot handle the problem of unlimited data flow in real situations, a transmission line component and its defect classification method based on adversarial continuous learning is proposed. In this paper, continuous learning technology is introduced into the task of transmission line component defect classification, so that the classification model can continuously learn new classification tasks from the infinite growth of data stream while ensuring the classification accuracy, and reduce the consumption of time and resources. By integrating attention mechanism, the ability of the model to extract subtle features is enhanced, the problem of small difference between classification tasks is solved, and the classification accuracy is improved. Focusing on the problem of sorting unknowability in continual learning tasks, a method of sorting based on discrete degree is proposed to achieve the optimal utilization of continual learning classification model. Finally, experiments are carried out on CIFAR-100 public data set and self built data set, and various performances of the model are analyzed and compared. The results show that the proposed method realizes the sustainable learning of component and defect classification task, and alleviates the problem of catastrophic forgetting. The accuracy of classification is improved by 1.43% and 2.25% respectively by integrating attention mechanism and using L₃ loss function. The optimal utilization of continuous learning classification model is realized, which lays a solid foundation for power grid security situational awareness.
- Transmission line situation awareness,
- Defect classification,
- Continual learning,
- Attention mechanism,
- Ranking agnosticism

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References(20)

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