基于扩展卡尔曼滤波的水中放电阶段辨识方法

doi:10.19595/j.cnki.1000-6753.tces.230336

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摘要

水中脉冲放电中的电弧通道发育具有强随机性,难以固定时域进行放电阶段划分,故水中脉冲放电阶段的辨识方法研究极为重要。该文建立了水中脉冲放电等效电路模型并阶段化分析了电阻时变特性,得到了判定放电阶段的临界参数阈值;采用扩展卡尔曼滤波算法将离散电阻数据连续化的同时,降低随机放电带来的噪声,从而获得辨识时变电阻模型;计算辨识电阻斜率,并依据斜率变化特性及变化系数K判定预击穿放电阶段和剧烈放电阶段;最终通过水中脉冲放电观测实验平台验证了有效性。结果表明,基于扩展卡尔曼滤波算法得到的水中脉冲放电电阻较试验值的方差小于1.717,电阻斜率方差小于1.899,故该方法可用于水中脉冲放电阶段的判定。

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关键词 ：水中脉冲放电, 时变电阻, 扩展卡尔曼滤波, 放电阶段辨识

Abstract：

High-voltage pulsed discharge in water is a complex plasma generation and collapse process involving coupled physical processes, such as electricity, light, and heat. The discharge process consists of pre-breakdown, violent, and oscillatory discharge stages. The main existing method for determine the discharge stage is by using high-speed camera photography, but this approach is not suitable for practical applications environment of pulsed discharge technology in water. Additionally, the development of the arc channel during repeated discharges exhibits strong randomness, making it difficult to classify discharge stages within a fixed time domain. Therefore, studying the identification method of the underwater discharge stage is crucial. This paper proposes a corresponding method based on the arc impedance change characteristics of the discharge in water.
Firstly, the plasma change characteristics observed at each discharge stage are amalgamated to elucidate the variations in resistance. The initial plasma channel is formed during the pre-breakdown discharge stage. It continues to ionize to form a more robust initial arc channel under the action of an external electric field, with a more stable and linear decline in resistance. Subsequently, a dense arc is established once the plasma density within the channel surpasses a critical threshold, leading to a sudden surge in plasma density and a rapid decrease in resistance. Nevertheless, with the diminishing strength of the external electric field, sustaining a balanced arc discharge becomes impracticable, resulting in the extinction of the arc. The channel retains a substantial plasma that gradually diminishes over time at this stage and makes the resistance progressive increase.
Secondly, the discharge process in water is mainly carried out in an inhomogeneous medium. Hence, the arc channel morphology and spatial location are random, and the resistance values within each discharge cycle in a repetitive discharge environment also vary. The Extended Kalman Filtering algorithm filters the measured resistance data against the randomness, thus obtaining a representative value of the identified resistance. Solving the slope of the resistance change at each moment and combining it with the characteristics of the changes in each discharge stage, a threshold value for the resistance change rate is derived. This threshold value serves as a criterion to determine the transition of the discharge stage.
Finally, the experimental platform for pulse discharge observation in water confirmed the effectiveness of the method, and an analysis of errors was conducted. Further experimental validation analyses were carried out at the biggest distance electrode gaps of 2.04 mm and 3.01 mm based on gap distances equal to 0.62 mm, 1.01 mm, and 1.38 mm. Additionally, the conductivity of the solution is increased to 4 685.13 μS/cm, 6 740.45 μS/cm, 7 820.62 μS/cm, 12 340.68 μS/cm, and 19 319.25 μS/cm, respectively. The results indicate that the variance of the identified resistance values obtained based on the Extended Kalman Filter algorithm for underwater pulse discharge is less than 1.717 compared to the experimentally measured resistance values. Moreover, the variances of the two type's resistance slopes are less than 1.899, with a relative error of the resistance slope being less than 0.089. Despite variations in electrode gap distance and solution conductivity, this method maintains high accuracy. Thus, it is suitable for determining discharge stages in the underwater pulse discharge process.

Key words： Pulse discharge in water time-varying resistance Extended Kalman Filtering (EKF) discharge stage identification

收稿日期: 2023-03-21

PACS:

TM89

基金资助:

国家重大科技专项（2016ZX05034004）和中国石油大学（华东）研究生创新基金（YCX2021109）资助项目

通讯作者: 康忠健男,1971年生,教授,博士生导师,研究方向为脉冲功率技术应用、脉冲谐波共振破岩技术。E-mail：kangzjzh@163.com

作者简介: 高崇男,1995年生,博士研究生,研究方向为脉冲功率技术应用及优化。E-mail：kmlggc@163.com

引用本文:

高崇, 邵在康, 康忠健, 傅雪原, 侯腾飞. 基于扩展卡尔曼滤波的水中放电阶段辨识方法[J]. 电工技术学报, 0, (): 239612-239612. Gao Chong, Shao Zaikang, Kang Zhongjian, Fu Xueyuan, Hou Tengfei. Method for Identifying Stages of Discharge in Water Based on Extended Kalman Filter. Transactions of China Electrotechnical Society, 0, (): 239612-239612.

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[1] 刘毅, 李志远, 李显东, 等. 水中脉冲激波对模拟岩层破碎试验[J]. 电工技术学报, 2016, 31(24): 71-78.
Liu Yi, Li Zhiyuan, Li Xiandong, et al.Experiments on the fracture of simulated stratum by underwater pulsed discharge shock waves[J]. Transactions of China Electrotechnical Society, 2016, 31(24): 71-78.
[2] 董冰岩, 甘青青, 孙宇, 等. 高压脉冲放电协同复合型催化剂去除甲醛的实验[J]. 电工技术学报, 2017, 32(8): 108-113.
Dong Bingyan, Gan Qingqing, Sun Yu, et al.Degradation of formaldehyde by high voltage pulse discharge combined with compound catalyst[J]. Transactions of China Electrotechnical Society, 2017, 32(8): 108-113.
[3] Smirnov A P, Zhekul V G, Taftai E I, et al.Effect of parameters of liquids on amplitudes of pressure waves generated by electric discharge[J]. Surface Engineering and Applied Electrochemistry, 2019, 55(1): 84-88.
[4] 王瑞雪, 李忠文, 虎攀, 等. 低温等离子体化学毒剂洗消技术研究进展[J]. 电工技术学报, 2021, 36(13): 2767-2781.
Wang Ruixue, Li Zhongwen, Hu Pan, et al.Review of research progress of plasma chemical warfare agents degradation[J]. Transactions of China Electrotechnical Society, 2021, 36(13): 2767-2781.
[5] 李元, 孙滢, 刘毅, 等. 液电效应及电火花震源的研究现状与展望[J]. 高电压技术, 2021, 47(3): 753-765.
Li Yuan, Sun Ying, Liu Yi, et al.Electrohydraulic effect and sparker source: current situation and prospects[J]. High Voltage Engineering, 2021, 47(3): 753-765.
[6] 王志强, 曹云霄, 邢政伟, 等. 高压脉冲放电破碎菱镁矿石的实验研究[J]. 电工技术学报, 2019, 34(4): 863-870.
Wang Zhiqiang, Cao Yunxiao, Xing Zhengwei, et al.Experimental study on fragmentation of magnesite ores by pulsed high-voltage discharge[J]. Transactions of China Electrotechnical Society, 2019, 34(4): 863-870.
[7] Kang Zhongjian, Gao Chong, Gong Dajian, et al.Effect and mechanism analysis of resonance on physical parameters of unconventional reservoirs[J]. Energy Reports, 2022, 8: 12522-12533.
[8] 戴栋, 宁文军, 邵涛. 大气压低温等离子体的研究现状与发展趋势[J]. 电工技术学报, 2017, 32(20): 1-9.
Dai Dong, Ning Wenjun, Shao Tao.A review on the state of art and future trends of atmospheric pressure low temperature plasmas[J]. Transactions of China Electrotechnical Society, 2017, 32(20): 1-9.
[9] 聂云良, 康忠健, 王聪, 等. 水中脉冲放电电极的烧蚀特性[J]. 高电压技术, 2021, 47(7): 2607-2614.
Nie Yunliang, Kang Zhongjian, Wang Cong, et al.Electrodes erosion characteristics of pulse discharge in water[J]. High Voltage Engineering, 2021, 47(7): 2607-2614.
[10] 黄仕杰, 刘毅, 林福昌, 等. 高压脉冲放电破岩电弧阻抗特性分析[J]. 电工技术学报, 2022, 37(19): 4978-4988.
Huang Shijie, Liu Yi, Lin Fuchang, et al.Analysis of arc impedance characteristics in high-voltage electric pulse discharge rock destruction[J]. Transactions of China Electrotechnical Society, 2022, 37(19): 4978-4988.
[11] Li Xiandong, Liu Yi, Liu Siwei, et al.Influence of deposited energy on shock wave induced by underwater pulsed current discharge[J]. Physics of Plasmas, 2016, 23(10): 103104.
[12] 刘毅, 黄仕杰, 赵勇, 等. 液中大电流脉冲放电电弧阻抗特性分析[J]. 高电压技术, 2021, 47(7): 2591-2598.
Liu Yi, Huang Shijie, Zhao Yong, et al.Analysis of arc impedance characteristics of high current pulsed discharge in liquid[J]. High Voltage Engineering, 2021, 47(7): 2591-2598.
[13] Tahir S, Raja M M, Razzaq N, et al.Extended Kalman filter-based power line interference canceller for electrocardiogram signal[J]. Big Data, 2022, 10(1): 34-53.
[14] 赵宏晨, 刘晓明, 李龙女. 采用遗传算法的低压开关电弧反演[J]. 高压电器, 2017, 53(4): 25-30.
Zhao Hongchen, Liu Xiaoming, Li Longnü.Arc inversion with genetic algorithm for low voltage switch[J]. High Voltage Apparatus, 2017, 53(4): 25-30.
[15] Liu Weijie, Zhou Hongliang, Tang Zeqiang, et al.State of charge estimation algorithm based on fractional-order adaptive extended Kalman filter and unscented Kalman filter[J]. Journal of Electrochemical Energy Conversion and Storage, 2022, 19(2): 021005.
[16] Greve C M, Hara K.Estimation of plasma properties using an extended Kalman filter with plasma global models[J]. Journal of Physics D: Applied Physics, 2022, 55(25): 255201.
[17] Ushakov V I.Impulse breakdown of liquids[M]. Berlin: Springer, 2007.
[18] Kolb J F, Joshi R P, Xiao S, et al.Streamers in water and other dielectric liquids[J]. Journal of Physics D: Applied Physics, 2008, 41(23): 234007.
[19] 王一博. 水中等离子体声源的理论与实验研究[D]. 长沙: 国防科学技术大学, 2012.
[20] 赵勇, 刘毅, 任益佳, 等. 水中大电流脉冲放电的激波传播特性[J]. 高电压技术, 2021, 47(3): 876-884.
Zhao Yong, Liu Yi, Ren Yijia, et al.Shock wave propagation characteristics of pulsed high-current discharge in water[J]. High Voltage Engineering, 2021, 47(3): 876-884.
[21] 李宁, 雷开卓, 黄建国, 等. 采用时变电阻的水下高压放电模型[J]. 高电压技术, 2009, 35(12): 3060-3064.
Li Ning, Lei Kaizhuo, Huang Jianguo, et al.Model of underwater high-voltage discharge using time-varying resistance[J]. High Voltage Engineering, 2009, 35(12): 3060-3064.
[22] 李显东, 刘毅, 李志远, 等. 不均匀电场下水中脉冲放电观测及沉积能量对激波的影响[J]. 中国电机工程学报, 2017, 37(10): 3028-3036.
Li Xiandong, Liu Yi, Li Zhiyuan, et al.Observation of underwater pulse discharge and influence of deposited energy on shock wave in non-uniform electric field[J]. Proceedings of the CSEE, 2017, 37(10): 3028-3036.
[23] (苏)卡兰塔罗夫, (苏)采依特林. 电感计算手册[M]. 陈汤铭等译. 北京: 机械工业出版社, 1992.
[24] 刘毅, 赵勇, 任益佳, 等. 水中大电流脉冲放电电弧通道发展过程分析[J]. 电工技术学报, 2021, 36(16): 3525-3534.
Liu Yi, Zhao Yong, Ren Yijia, et al.Analysis on the development process of arc channel for underwater high current pulsed discharge[J]. Transactions of China Electrotechnical Society, 2021, 36(16): 3525-3534.
[25] 王竞才, 李琰, 徐天奇. 基于扩展卡尔曼滤波的智能电网虚假数据检测[J]. 智慧电力, 2022, 50(3): 50-56.
Wang Jingcai, Li Yan, Xu Tianqi.Detection of false data in smart grid based on extended Kalman filter[J]. Smart Power, 2022, 50(3): 50-56.
[26] 胡敬宇, 汪龑, 严永俊, 等. 基于限定记忆随机加权扩展卡尔曼滤波的车辆状态估计[J]. 东南大学学报(自然科学版), 2022, 52(2): 387-393.
Hu Jingyu, 汪龑, Yan Yongjun, et al. Vehicle state estimation based on limited memory random weighted extended Kalman filter[J]. Journal of Southeast University (Natural Science Edition), 2022, 52(2): 387-393.
[27] 喻越, 朱鑫磊, 黄昆, 等. 应用于石油解堵增产的水中脉冲放电特性实验研究[J]. 高电压技术, 2020, 46(8): 2951-2959.
Yu Yue, Zhu Xinlei, Huang Kun, et al.Experimental study on pulse discharge characteristics in water applied to oil plugging and increasing production[J]. High Voltage Engineering, 2020, 46(8): 2951-2959.