基于改进脉冲耦合神经网络模型的真空电弧燃烧过程研究

doi:10.19595/j.cnki.1000-6753.tces.181158

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摘要真空开关开断电流过程中电弧形态演变和特征量变化过程是决定真空开关开断能力的关键特性之一。该文首先建立改进脉冲耦合神经网络（PCNN）模型,对真空电弧图像进行多值分割;然后利用形态学技术对分割电弧图像进行连通域选取和边缘噪声滤波,用于量化描述动触头位移、电弧面积等特征参数;最后结合特征参数曲线和电弧实验图像,对整个开断过程中真空电弧产生、发展和熄灭等燃烧过程展开定量和定性分析。研究结果表明,改进PCNN模型适用于处理边缘区域灰度梯度变化大的真空电弧图像,呈现出细节特征丰富、边缘清晰、噪声低和分割精度高等特点;结合电弧特征参数和实验结果对电弧燃烧过程进行定量和定性分析,相比前期研究工作更加细化深入;不同峰值电弧电流对电弧面积、电弧最大面积出现时刻、电弧各燃烧阶段持续时间和转变过程均有很大影响。本文将定量计算和定性分析相结合,较深入地研究了真空开关开断过程中电弧的燃烧过程,研究工作对真空电弧调控策略的提出具有一定的参考价值。

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	向川
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	史鹏飞
	董华军

关键词 ：真空电弧, 燃烧过程, 脉冲耦合神经网络, 形态学技术

Abstract：The combustion process of Vacuum arc, such as the evolution of arc shape and the change of characteristic parameters during the breaking process of short current, is one of the key characteristics that determines the breaking capacity of vacuum circuit breakers(VCBs). In this study, an improved pulse coupled neural network (PCNN) model was firstly established to perform multi-value segmentation of vacuum arc images. Then, the morphological technique was used to select the connected domain and filter the edge noise of the segmented arc image, the characteristic parameters, such as the displacement of the moving contact and the arc area, were quantified. Finally, combining the characteristic parameter curves and the arc experiment images, the combustion process of vacuum arc during the entire breaking process, including the generation, development and extinction of vacuum arc, were analyzed quantitatively and qualitatively. The research results illustrated that the improved PCNN model showed rich details and features, distinct edges, low noise and high accuracy of segmentation, it was suitable to process vacuum arc images which have large gradient changes of gray in the edge region. Based on the arc characteristic parameters and experimental results, the quantitative and qualitative analysis of the arc combustion process was more detailed and deeper than our previous research work. The peak arc current had a great influence on the arc area, the moment of maximum arc area, the duration of the arc combustion stages and the transformation process. This paper combines quantitative and qualitative research results, and deeply studies the combustion process of arc during the breaking process of VCBs, which can provide reference for vacuum arc regulation strategy.

Key words： Vacuum arc combustion process pulse coupled neural network morphology technology

收稿日期: 2018-07-04 出版日期: 2019-10-12

PACS:

TM561.2

基金资助:国家自然科学基金（51507025、51477023）,辽宁省自然科学基金（20180550822）和中央高校业务经费专项资金（3132016223）资助项目

通讯作者: 向川男,1982年生,讲师,研究方向为智能高压电器、高电压新技术。E-mail：cxiang@dlmu.edu.cn

作者简介: 王惠女,1995年生,硕士研究生,研究方向为高电压新技术。E-mail：18340829151@163.com

引用本文:

向川, 王惠, 史鹏飞, 董华军. 基于改进脉冲耦合神经网络模型的真空电弧燃烧过程研究[J]. 电工技术学报, 2019, 34(19): 4028-4037. Xiang Chuan, Wang Hui, Shi Pengfei, Dong Huajun. Research on the Combustion Process of Vacuum Arc Based on an Improved Pulse Coupled Neural Network Model. Transactions of China Electrotechnical Society, 2019, 34(19): 4028-4037.

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https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.181158 https://dgjsxb.ces-transaction.com/CN/Y2019/V34/I19/4028

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