基于生成对抗网络的有载调压开关故障检测研究

doi:10.3976/j.issn.1002-4026.2023.06.009

Abstract

Abstract:

The probability of power transformer failure is extremely low, which leads to a great impact on further in-depth analysis results due to unbalanced data when processing transformer fault data. To solve these problems, this study processes and judges the unbalanced data using an confrontation neural network combined with an artificial neural network, uses the distributed acoustic wave sensing technology based on ultraweak fiber Bragg gratings to collect and analyze the data of the simulation site of the transformer built in a laboratory, and achieves good results on the collected transformer fault simulation data. This method has an important referential significance for developing the small sample fault identification system of the on-load transformer using confrontation generation network.

Key words: artificial neural network, point sensor, on-load transformer, fault detection, pattern recognition, generative adversarial networks, data enhancement

CLC Number:

TN247

JIANG Xiaodong, WANG Leilei, SUN Peng, YANG Guang, GENG Junqi, WANG Jiawen, HUANG Sheng, QU Shuai, WANG Chen, SHANG Ying. Fault detection of an on-load tap changer based on generative adversarial network[J].Shandong Science, 2023, 36(6): 68-73.

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Fault detection of an on-load tap changer based on generative adversarial network

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