基于强跟踪滤波器的水中高频振荡放电参数分析

doi:10.19595/j.cnki.1000-6753.tces.230777

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1804 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Underwater pulsed discharge is a complex physical process characterized. Based on the characteristics of plasma evolution, this process is divided into pre-breakdown discharge, main discharge, and high-frequency oscillation discharge stages. Existing research focuses on the impact of energy conversion during the pre-breakdown and main discharge stages, with the relatively limited investigation into parameters during the high-frequency oscillation discharge stage. The distribution of the electric field between electrodes is susceptible to environmental influences in the underwater pulsed discharge process, leading to a significant level of randomness in the development of arc channe. Therefore, the circuit parameters of single discharge data is not representative. This paper proposes a method for analyzing parameters of high-frequency oscillation discharge underwater by using a strong tracking filter (STF).
Firstly, the variations of resistance and inductance were delineated based on the plasma dynamics in the high-frequency oscillation discharge stage, and the equivalent circuit model was established. Plasma density within the bubbles generated in the main discharge stage remains substantial and retains conductivity during this stage. Under the residual voltage of the capacitors, the equivalent capacitance and inductance induce attenuated oscillatory perturbations within the discharge circuit. At this stage, due to the loss of the arc channel, the bubble resembles a non-heat source structure, which means this bubble cannot continue to expand under the action of static water pressure, and the volume of it begins to pulsate regularly. The plasma density in the bubble is influenced by this phenomenon, resulting in oscillatory variations in the inter-electrode resistance and inductance. Constrained by the electrode configuration and its inherent morphological characteristics, the bubble undergo collapse, leading to a rapid increase in the inter-electrode resistance during this process.
Next, the underwater pulsed discharge environment results in a non-uniform electric field within the fluid field between discharge electrodes, leading to stochastic characteristics in the morphology and spatial position of the plasma arc channel. This discharge characteristic makes variations in resistance and inductance values among different high-frequency oscillation discharge cycles, concurrently the variation trend of parameters in the time domain is also different. To obtain practical resistance values, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to separate the inductance from the measured data. The obtained resistance and inductance data to filtering using the STF, thereby yielding representative identification values for resistance and inductance.
Finally, the efficacy of the proposed method was validated followed by a subsequent analysis of the results' discreteness. To reflect the reliability of the discharge resistance and inductance values identified by the STF method, the discreteness coefficient is introduced as the quantization parameter. The research findings indicate that, during the expansion stage of bubble formation in the high-frequency oscillation discharge process, the deviations in both resistance and inductance are relatively minor, measuring less than 2.83% and 0.61%, respectively. In the bubble pulsation stage, the deviations in resistance and inductance are more significantly influenced by the morphology of the bubble, with the trend of deviation coefficient variations displaying a distinct pulsating pattern, the deviations are less than 23.26% and 1.59% for resistance and inductance, respectively. In the bubble collapse stage, the parameters are strongly affected by the stochastic nature of bubble deformation, resulting in the highest level of deviation in this phase, the maximum deviation coefficients are 110% and 24.32%, respectively.

Key words： Underwater pulse discharge high frequency oscillation identification of parameters complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) strong tracking filter

Received: 29 May 2023

PACS:

TM89

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.230777 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I13/4090

[1] 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.
[2] Liu Yi, Ren Yijia, Liu Siwei, et al.Comparison and analysis of shockwave characteristics between underwater pulsed discharge and metal wire explosion[J]. Physics of Plasmas, 2020, 27(3): 033503.
[3] 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.
[4] 刘友波, 吴浩, 刘挺坚, 等. 集成经验模态分解与深度学习的用户侧净负荷预测算法[J]. 电力系统自动化, 2021, 45(24): 57-64.
Liu Youbo, Wu Hao, Liu Tingjian, et al.User-side net load forecasting method integrating empirical mode decomposition and deep learning[J]. Automation of Electric Power Systems, 2021, 45(24): 57-64.
[5] Hu Chenjia, Zhao Yan, Jiang He, et al.Prediction of ultra-short-term wind power based on CEEMDAN-LSTM-TCN[J]. Energy Reports, 2022, 8(12): 483-492.
[6] Hu Yanjuan, Ouyang Yi, Wang Zhanli, et al.Vibration signal denoising method based on CEEMDAN and its application in brake disc unbalance detection[J]. Mechanical Systems and Signal Processing, 2023, 187: 109972.
[7] 费鸿禄, 山杰. CEEMDAN-小波阈值法在爆破振动信号处理中的应用[J]. 爆破, 2022, 39(3): 41-47, 164.
Fei Honglu, Shan Jie.Application of CEEMDAN-wavelet threshold method in blasting vibration signal processing[J]. Blasting, 2022, 39(3): 41-47, 164.
[8] 高崇, 邵在康, 康忠健, 等. 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, 2024, 39(11): 3475-3485.
Gao Chong, Shao Zaikang, Kang Zhongjian, et al.Method for identifying stages of discharge in water based on extended Kalman filter[J]. Transactions of China Electrotechnical Society, 2024, 39(11): 3475-3485.
[9] Cruccolini V, Scarcelli R, Battistoni M, et al.Multidimensional modeling of non-equilibrium plasma generated by a radio-frequency corona discharge[J]. Plasma Sources Science and Technology, 2020, 29(11): 115013.
[10] 邵涛, 李帅康, 黄邦斗, 等. 高重复频率纳秒脉冲放电特性[J]. 南昌大学学报(理科版), 2021, 45(6): 520-524.
Shao Tao, Li Shuaikang, Huang Bangdou, et al.Characteristics of high-repetition-rate nanosecond pulsed discharge[J]. Journal of Nanchang University (Natural Science), 2021, 45(6): 520-524.
[11] 赵政, 李晨颉, 张幸, 等. 重复频率脉冲流注放电演变现象与机制研究进展[J]. 强激光与粒子束, 2021, 33(6): 19-29.
Zhao Zheng, Li Chenjie, Zhang Xing, et al.Research progress on evolution phenomena and mechanisms of repetitively pulsed streamer discharge[J]. High Power Laser and Particle Beams, 2021, 33(6): 19-29.
[12] Shen Kelei, Ni Hongyu, Lu Qiang, et al.Power system state estimation based on improved strong tracking unscented Kalman filter[C]//2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE), Chongqing, China, 2022: 1-4.
[13] 肖贤贵, 李开成, 蔡得龙, 等. 一种电能质量扰动信号的联合去噪算法[J]. 电工技术学报, 2021, 36(21): 4418-4428.
Xiao Xiangui, Li Kaicheng, Cai Delong, et al.A combined de-noising method for power quality disturbances events[J]. Transactions of China Electro-technical Society, 2021, 36(21): 4418-4428.
[14] Wang Yulu, Chiang H D, Dong Na.Power-line partial discharge recognition with Hilbert-Huang transform features[J]. Energies, 2022, 15(18): 6521.
[15] Lebedev Y A, Tatarinov A V, Epshtein I L, et al.One-dimensional simulation of microwave discharge in a gas bubble in water[J]. High Energy Chemistry, 2021, 55(6): 507-518.
[16] Gao Xiaozhi, Wang Lei, Sun Huiqin, et al.Research on optimal configuration of hybrid energy storage system based on improved CEEMDAN[J]. Energy Reports, 2021, 7: 1308-1318.
[17] 王艳松, 衣良. 基于小波变换和强跟踪滤波器的配电网短路故障定位[J]. 电力系统保护与控制, 2013, 41(15): 76-81.
Wang Yansong, Yi Liang.Location for interphase short circuit fault in distribution network based on wavelet transforms and strong tracking filter[J]. Power System Protection and Control, 2013, 41(15): 76-81.
[18] 包伟川, 宿浩, 李璐, 等. 基于电流-振动信号联合分析的高压断路器操动机构故障诊断方法[J]. 高压电器, 2023, 59(8): 164-172.
Bao Weichuan, Su Hao, Li Lu, et al.Fault diagnosis method of operating mechanism for high voltage circuit breaker based on combined analysis of current and vibration signals[J]. High Voltage Apparatus, 2023, 59(8): 164-172.
[19] Mousavi A A, Zhang Chunwei, Masri S F, et al.Structural damage localization and quantification based on a CEEMDAN Hilbert transform neural network approach: a model steel truss bridge case study[J]. Sensors, 2020, 20(5): 1271.
[20] 牛胜锁, 王春鑫, 梁志瑞, 等. 基于改进强跟踪无迹卡尔曼滤波的电力信号同步相量跟踪算法[J]. 电工技术学报, 2021, 36(11): 2255-2264.
Niu Shengsuo, Wang Chunxin, Liang Zhirui, et al.An algorithm for tracking synchronous phasor of power signals based on improved strong tracking unscented Kalman filter[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2255-2264.
[21] Alaaudeen K M, Aruna T, Ananthi G.An improved strong tracking Kalman filter algorithm for real-time vehicle tracking[J]. Materials Today: Proceedings, 2022, 64: 931-939.
[22] Wei Jingwen, Dong Guangzhong, Chen Zonghai.Model-based fault diagnosis of lithium-ion battery using strong tracking extended Kalman filter[J]. Energy Procedia, 2019, 158: 2500-2505.
[23] Wang Qinghui, Huang Wangyuan, Wei Lifeng, et al.A wireless localization algorithm based on strong tracking Kalman filter[J]. Sensors and Transducers, 2014, 183(12): 155-161.