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.

Figures/Tables 6

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References 14

[1]	林明霞. 一种实现φ-OTDR系统长距离监测的方法及系统: CN104457808A[P]. 2015-03-25.
[2]	张知先, 陈伟根, 汤思蕊, 等. 基于互补集总经验模态分解和局部异常因子的有载分接开关状态特征提取及异常状态诊断[J]. 电工技术学报, 2019, 34(21): 4508-4518. DOI: 10.19595/j.cnki.1000-6753.tces.181320.
[3]	聂鹏. 浅谈电力变压器故障分析及其诊断方法[J]. 科技创新与应用, 2014(12): 121.
[4]	赵彤. 有载分接开关机械状态的在线监测与故障诊断技术研究[D]. 济南: 山东大学, 2008.
[5]	钟龙申, 高学军, 王振友. 一种新的基于K-means改进SMOTE算法在不平衡数据集分类中的应用[J]. 数学的实践与认识, 2015, 45(19): 198-206.
[6]	DOUZAS G, BACAO F, LAST F. Improving imbalanced learning through a heuristic oversampling method based on K-means and SMOTE[J]. Information Sciences, 2018, 465: 1-20. DOI: 10.1016/j.ins.2018.06.056.
[7]	WU H J, CHEN J P, LIU X R, et al. One-dimensional CNN-based intelligent recognition of vibrations in pipeline monitoring with DAS[J]. Journal of Lightwave Technology, 2019, 37(17): 4359-4366. DOI: 10.1109/JLT.2019.2923839.
[8]	SHI Y, WANG YY, WANG L Y, et al. Multi-event classification for Φ-OTDR distributed optical fiber sensing system using deep learning and support vector machine[J]. Optik, 2020, 221: 165373. DOI: 10.1016/j.ijleo.2020.165373.
[9]	SHI Y, LI Y H, ZHANG Y C, et al. An easy access method for event recognition of Φ-OTDR sensing system based on transfer learning[J]. Journal of Lightwave Technology, 2021, 39(13): 4548-4555. DOI: 10.1109/JLT.2021.3070583.
[10]	JIANG F, LI H L, ZHANG Z H, et al. An event recognition method for fiber distributed acoustic sensing systems based on the combination of MFCC and CNN[C]∥ 2017 International Conference on Optical Instruments and Technology:Advanced Optical Sensors and Applications. Beijing, China: SPIE, 2018, 10618: 15-21. DOI:10.1117/12.2286220.
[11]	董贯雷, 姜晓东, 孙鹏, 等. 基于分布式光纤传感系统的有载变压器故障检测技术研究[J]. 山东科学, 2023, 36(5): 52-59. DOI:10.3976/j.issn.1002-4026.2023.05.007.
[12]	陈先昌. 基于卷积神经网络的深度学习算法与应用研究[D]. 杭州: 浙江工商大学, 2014.
[13]	刘刚坤, 李宝福. 基于光纤布拉格光栅的多点压力测量[J]. 计量与测试技术, 2022, 49(8): 57-60. DOI: 10.15988/j.cnki.1004-6941.2022.8.016.
[14]	张跃, 刘伟, 胡波, 等. 一种基于GAN网络的样本数据增强方法及系统: CN113962360A[P]. 2022-01-21.

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

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