Abstract：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: 0~400 μs, containing the whole breakdown waveform, and 0~200 μs, 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 (1 530) in 0~400 μs and 0~200 μs were for the breakdown mechanism classification training, and breakdown waveforms of C, D and E (1 398) in 0~400 μs and 0~200 μs 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 (0~200 μs) were 88.92%, 87.99% and 92.78%, respectively, and all the accuracies of 0~200 μs were higher than 87.99%. The breakdown mechanism classification accuracies of C, D and E (0~400 μs) were 85.23%, 84.90% and 91.90%, respectively. Compared with 0~400 μs, the breakdown mechanism classification accuracies of 0~200 μs were improved by 3.69%, 3.09% and 0.88%, respectively. The accuracy of 0~200 μs had an average improvement by 2.55% than that of 0~400 μs. Precision, recall and F1-score of 0~200 μs were also higher than those of 0~400 μs. The results showed that 0~200 μs, 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.
李世民, 徐勋晨, 张潮海. 基于深度学习的冲击电压老炼过程中真空击穿机制甄别优化方法[J]. 电工技术学报, 2024, 39(13): 4153-4163.
Li Shimin, Xu Xunchen, Zhang Chaohai. Optimization Method for Classifying Breakdown Mechanism During Impulse Voltage Conditioning Process Based on Deep Learning. Transactions of China Electrotechnical Society, 2024, 39(13): 4153-4163.
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2024, Vol. 39 
