Research on Power Quality Disturbance Signal Classification Based on Random Matrix Theory

被引：0

作者：

Liu, Keyan ^{[1
]}

Jia, Dongli ^{[1
]}

He, Kaiyuan ^{[1
]}

Zhao, Tingting ^{[2
]}

Zhao, Fengzhan ^{[2
]}

机构：

[1] China Elect Power Res Inst, Beijing, Peoples R China

[2] China Agr Univ, Coll Informat & Elect Engn, Beijing, Peoples R China

来源：

DATA SCIENCE, PT II | 2017年 / 728卷

关键词：

Power quality disturbance; Random matrix theory; Mean spectral radius; WAVELET TRANSFORM;

D O I：

10.1007/978-981-10-6388-6_30

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a method of power quality disturbance classification based on random matrix theory (RMT) is proposed. The method utilizes the power quality disturbance signal to construct a random matrix. By analyzing the mean spectral radius (MSR) variation of the random matrix, the type and time of occurrence of power quality disturbance are classified. In this paper, the random matrix theory is used to analyze the voltage sag, swell and interrupt perturbation signals to classify the occurrence time, duration of the disturbance signal and the depth of voltage sag or swell. Examples show that the method has strong anti-noise ability.

引用

页码：365 / 376

页数：12

共 18 条

[1] Bai ZD, 2010, SPRINGER SER STAT, P433, DOI 10.1007/978-1-4419-0661-8_12
[2] Cross-Spectrum Analysis-Based Scheme for Multiple Power Quality Disturbance Sensing Device
Dalai, Sovan
Dey, Debangshu
Chatterjee, Biswendu
Chakravorti, Sivaji
Bhattacharya, Kesab
[J]. IEEE SENSORS JOURNAL, 2015, 15 (07) : 3989 - 3997
[3] Guionnet A, 2009, ARXIV E PRINTS
[4] Framework of Random Matrix Theory for Power System Data Mining in a Non-Gaussian Environment
Han, Bei
Luo, Lingen
Sheng, Gehao
Li, Guojie
Jiang, Xiuchen
[J]. IEEE ACCESS, 2016, 4 : 9969 - 9977
[5] A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory
He, Xing
Ai, Qian
Qiu, Robert Caiming
Huang, Wentao
Piao, Longjian
Liu, Haichun
[J]. IEEE TRANSACTIONS ON SMART GRID, 2017, 8 (02) : 674 - 686
[6] Designing for Situation Awareness of Future Power Grids: An Indicator System Based on Linear Eigenvalue Statistics of Large Random Matrices
He, Xing
Qiu, Robert Caiming
Ai, Qian
Chu, Lei
Xu, Xinyi
Ling, Zenan
[J]. IEEE ACCESS, 2016, 4 : 3557 - 3568
[7] Symmetrical Components-Based Modified Technique for Power-Quality Disturbances Detection and Classification
Kumar, Raj
Singh, Bhim
Shahani, D. T.
[J]. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2016, 52 (04) : 3443 - 3450
[8] Detection and Classification of Power Quality Disturbances Using Double Resolution S-Transform and DAG-SVMs
Li, Jianmin
Teng, Zhaosheng
Tang, Qiu
Song, Junhao
[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2016, 65 (10) : 2302 - 2312
[9] [刘威 Liu Wei], 2016, [中国电机工程学报, Proceedings of the Chinese Society of Electrical Engineering], V36, P4854
[10] A Classification Method for Complex Power Quality Disturbances Using EEMD and Rank Wavelet SVM
Liu, Zhigang
Cui, Yan
Li, Wenhui
[J]. IEEE TRANSACTIONS ON SMART GRID, 2015, 6 (04) : 1678 - 1685

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