Parameters Identification for Extended Debye Model of XLPE Cables Based on Sparsity-Promoting Dynamic Mode Decomposition Method

被引:0
|
作者
Wang, Weijun [1 ]
Chen, Min [1 ]
Yin, Hui [1 ]
Li, Yuan [2 ]
机构
[1] Guiyang Power Supply Bureau, Guizhou Power Grid Co., Ltd, Guiyang,550004, China
[2] College of Electrical Engineering, Sichuan University, Chengdu,610065, China
关键词
Cable sheathing - Depolarization - Dielectric devices - Dielectric losses - Frequency domain analysis - Insulation - Parameter estimation - Phonons - Time domain analysis;
D O I
10.32604/ee.2023.028620
中图分类号
学科分类号
摘要
To identify the parameters of the extended Debye model of XLPE cables, and therefore evaluate the insulation performance of the samples, the sparsity-promoting dynamic mode decomposition (SPDMD) method was introduced, as well the basics and processes of its application were explained. The amplitude vector based on polarization current was first calculated. Based on the non-zero elements of the vector, the number of branches and parameters including the coefficients and time constants of each branch of the extended Debye model were derived. Further research on parameter identification of XLPE cables at different aging stages based on the SPDMD method was carried out to verify the practicability of the method. Compared with the traditional differential method, the simulation and experiment indicated that the SPDMD method can effectively avoid problems such as the relaxation peak being unobvious, and possessing more accuracy during the parameter identification. And due to the polarization current being less affected by the measurement noise than the depolarization current, the SPDMD identification results based on the polarization current spectral line proved to be better at reflecting the response characteristics of the dielectric. In addition, the time domain polarization current test results can be converted into the frequency domain, and then used to obtain the dielectric loss factor spectrum of the insulation. The integral of the dielectric loss factor on a frequency domain can effectively evaluate the insulation condition of the XLPE cable. © 2023, Tech Science Press. All rights reserved.
引用
收藏
页码:2433 / 2448
相关论文
共 32 条
  • [1] Sparsity-promoting dynamic mode decomposition
    Jovanovic, Mihailo R.
    Schmid, Peter J.
    Nichols, Joseph W.
    [J]. PHYSICS OF FLUIDS, 2014, 26 (02)
  • [2] Sparsity-Promoting Dynamic Mode Decomposition of Plasma Turbulence
    Kusaba, Akira
    Kuboyama, Tetsuji
    Inagaki, Shigeru
    [J]. PLASMA AND FUSION RESEARCH, 2020, 15
  • [3] Sparsity-Promoting Dynamic Mode Decomposition for Systems with Inputs
    Annoni, Jennifer
    Seiler, Peter
    Jovanovic, Mihailo R.
    [J]. 2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC), 2016, : 6506 - 6511
  • [4] Sparsity-promoting dynamic mode decomposition of plasma turbulence
    Kusaba, Akira
    Kuboyama, Tetsuji
    Inagaki, Shigeru
    [J]. Plasma and Fusion Research, 2020, 15 : 1 - 4
  • [5] Multilevel method for predicting flow fields in radial turbines based on sparsity-promoting dynamic mode decomposition
    Zheng, Mingqiu
    Hu, Chenxing
    Yang, Ce
    [J]. INTERNATIONAL JOURNAL OF NUMERICAL METHODS FOR HEAT & FLUID FLOW, 2023, 33 (10) : 3327 - 3352
  • [6] Analysis of Coherent Phonon Signals by Sparsity-promoting Dynamic Mode Decomposition
    Murata, Shin
    Aihara, Shingo
    Tokuda, Satoru
    Iwamitsu, Kazunori
    Mizoguchi, Kohji
    Akai, Ichiro
    Okada, Masato
    [J]. JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN, 2018, 87 (05)
  • [7] Evaluation of Optimization Algorithms and Noise Robustness of Sparsity-Promoting Dynamic Mode Decomposition
    Iwasaki, Yuto
    Nonomura, Taku
    Nakai, Kumi
    Nagata, Takayuki
    Saito, Yuji
    Asai, Keisuke
    [J]. IEEE ACCESS, 2022, 10 : 80748 - 80763
  • [8] Estimation and Control of Fluid Flows Using Sparsity-Promoting Dynamic Mode Decomposition
    Tsolovikos, Alexandros
    Bakolas, Efstathios
    Suryanarayanan, Saikishan
    Goldstein, David
    [J]. IEEE CONTROL SYSTEMS LETTERS, 2021, 5 (04): : 1145 - 1150
  • [9] Anti-circulant dynamic mode decomposition with sparsity-promoting for highway traffic dynamics analysis
    Wang, Xudong
    Sun, Lijun
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2023, 153
  • [10] Simultaneous identification of linear building dynamic model and disturbance using sparsity-promoting optimization
    Zeng, Tingting
    Brooks, Jonathan
    Barooah, Prabir
    [J]. AUTOMATICA, 2021, 129