摘要
冲击电压老炼技术是提高真空电极间隙绝缘能力的有效手段,快速准确地甄别真空击穿机制对揭示冲击电压老炼过程的物理演化机理具有重要意义。该文提出了一种基于深度学习的通过突显击穿前过程提高真空击穿机制甄别准确度的优化方法。对五对相同的无氧铜球形电极开展同样的冲击电压老炼试验,分别获得时间提取范围为0~400μs的完整击穿电压电流波形样本和0~200μs的突显击穿前过程的击穿电压电流波形样本,并通过深度学习模型对两种击穿电压电流波形样本开展脉冲电流诱发、场致发射诱发和微粒诱发三种真空击穿机制的甄别训练与测试,并将测试结果与真实结果进行对比分析与评估。结果显示:时间提取范围为0~200μs的突显击穿前过程的击穿电压电流波形样本的击穿机制甄别准确率均在87.99%以上,平均提高了2.55%,其对应的精确率、召回率和F1分数均更优。研究结果表明,突显击穿前过程的击穿电压电流波形处理能够有效地优化真空击穿机制甄别的效果,具有良好的工程应用前景。
Impulse voltage conditioning technology is an effective means to improve the insulation ability of vacuum circuit breaker(VCB).Classifying the breakdown mechanism quickly and accurately has a great significance to reveal the physical evolution of impulse voltage conditioning and improve the VCB withstanding voltage level.The traditional method to classify the breakdown mechanism needs to eliminate the displacement current through mathematical compensation algorithm and fit the Fowler-Nordheim formula,which is complicated to obtain the breakdown mechanism.Deep learning has an obvious advantage in image recognition and feature extraction.In this paper,an optimized method to classify the breakdown mechanism was proposed through enlarging the pre-breakdown period in breakdown waveform based on deep learning.Five identical sphere oxygen-free copper electrode pairs A,B,C,D and E were applied the same impulse conditioning.All the breakdown waveforms were processed into two kinds:0~400μs,containing the whole breakdown waveform,and 0~200μs,pre-breakdown period enlarged breakdown waveform.The corresponding breakdown mechanisms of A and B were labeled as pulsed current induced vacuum breakdown(PB),field emission induced breakdown(FEBD)and particle induced vacuum breakdown(PBD)through the traditional method.Then,breakdown waveforms of A and B(1530)in 0~400μs and 0~200μs were for the breakdown mechanism classification training,and breakdown waveforms of C,D and E(1398)in 0~400μs and 0~200μs were for breakdown mechanism classification test,respectively.The corresponding breakdown mechanisms of C,D and E were classified into PB,FEBD and PBD with deep learning.In addition,the breakdown mechanisms of C,D and E were also obtained through the traditional method.The deep learning outputs were compared with that through the traditional method.The test results were evaluated and analyzed by the evaluation parameters such as precision,recall,F1-score and so on.The results showed that the breakdown mechanism classification accuracies of C,D and E(0~200μs)were 88.92%,87.99%and 92.78%,respectively,and all the accuracies of 0~200μs were higher than 87.99%.The breakdown mechanism classification accuracies of C,D and E(0~400μs)were 85.23%,84.90%and 91.90%,respectively.Compared with 0~400μs,the breakdown mechanism classification accuracies of 0~200μs were improved by 3.69%,3.09%and 0.88%,respectively.The accuracy of 0~200μs had an average improvement by 2.55%than that of 0~400μs.Precision,recall and F1-score of 0~200μs were also higher than those of 0~400μs.The results showed that 0~200μs,pre-breakdown period enlarged breakdown waveform had a better performance in breakdown mechanism classification.Conclusions were drawn as following:(1)The classification accuracy for breakdown mechanism through deep learning could be improved by enlarging the pre-breakdown period in the breakdown waveform.(2)The breakdown mechanism classification can be completed quickly and accurately,whose accuracy could be higher than 87.99%with the effectiveness verified by precision,recall and F1-score.It has a theoretical guidance for a promising conditioning technology to improve the VCB voltage level in industry application.
作者
李世民
徐勋晨
张潮海
Li Shimin;Xu Xunchen;Zhang Chaohai(Center for More-Electric-Aircraft Power System Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处
《电工技术学报》
EI
CSCD
北大核心
2024年第13期4153-4163,共11页
Transactions of China Electrotechnical Society
基金
国家自然科学基金(52207162)
江苏省自然科学基金(BK20210307)
中央高校基本科研业务费专项资金(NJ2023012,NJ2023014)资助项目。
关键词
冲击电压老炼
击穿机制
深度学习
突显击穿前过程
击穿波形
Impulse voltage conditioning
breakdown mechanism
deep learning
pre-breakdown period enlarged
breakdown waveform
