Power Supply Risk Identification Method of Active Distribution Network Based on Transfer Learning and CBAM-CNN

Hengyu Liu (), Jiazheng Sun, Yongchao Pan, Dawei Hu, Lei Song, Zishang Xu, Hailong Yu and Yang Liu
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Hengyu Liu: Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd., Shenyang 110055, China
Jiazheng Sun: Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd., Shenyang 110055, China
Yongchao Pan: State Grid Liaoning Electric Power Supply Co., Ltd., Shenyang 110004, China
Dawei Hu: Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd., Shenyang 110055, China
Lei Song: State Grid Zhejiang Electric Power Co., Ltd., Marketing Service Center, Hangzhou 310007, China
Zishang Xu: China Electric Power Research Institute Co., Ltd., Beijing 100192, China
Hailong Yu: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Yang Liu: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China

Energies, 2024, vol. 17, issue 17, 1-17

Abstract: With the development of the power system, power users begin to use their own power supply in order to improve the power economy, but this also leads to the occurrence of the risk of self-provided power supply. The actual distribution network has few samples of power supply risk and it is difficult to identify the power supply risk by using conventional deep learning methods. In order to achieve high accuracy of self-provided power supply risk identification with small samples, this paper proposes a combination of transfer learning, convolutional block attention module (CBAM), and convolutional neural network (CNN) to identify the risk of self-provided power supply in an active distribution network. Firstly, in order to be able to further identify whether or not a risk will be caused based on completing the identification of the faulty line, we propose that it is necessary to identify whether or not the captive power supply on the faulty line is in operation. Second, in order to achieve high-precision identification and high-efficiency feature extraction, we propose to embed the CBAM into a CNN to form a CBAM-CNN model, so as to achieve high-efficiency feature extraction and high-precision risk identification. Finally, the use of transfer learning is proposed to solve the problem of low risk identification accuracy due to the small number of actual fault samples. Simulation experiments show that compared with other methods, the proposed method has the highest recognition accuracy and the best effect, and the risk recognition accuracy of active distribution network backup power is high in the case of fewer samples.

Keywords: active distribution network; transfer learning; convolutional block attention module; convolutional neural network; power supply risk identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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