Online identification and evaluation algorithm of abnormal disturbance of traction power supply system

被引：0

作者：

Yu J. ^{[1
]}

Hu H. ^{[1
]}

Tao H. ^{[1
]}

机构：

[1] School of Electrical Engineering, Southwest Jiaotong University, Chengdu

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2023年 / 43卷 / 08期

基金：

中国国家自然科学基金;

关键词：

abnormal disturbance identification; Hilbert transform; severity evaluation; singular spectrum analysis; TLS-ESPRIT algorithm; traction power supply system;

D O I：

10.16081/j.epae.202212021

中图分类号：

学科分类号：

摘要：

The transient／steady-state overvoltage and overcurrent generated by abnormal electrical disturbances in the traction power supply system have the characteristics of similar features，large frequency separation range，and difficulty in capturing. These characteristics make it difficult to determine the type of disturbance events and identify the key modes and parameters of the disturbance. Therefore，a feature extraction algorithm based on singular spectrum analysis and Hilbert transform is proposed，which realizes the online rapid identification of different abnormal disturbance types. Then the total least squares-estimation of signal parameters via rotational invariance techniques（TLS-ESPRIT） algorithm is improved to accurately evaluate the disturbance modal parameters. According to the characteristics of each disturbance and the key modal parameters of the disturbance，the evaluation index of disturbance severity is defined. The above algorithms are used to identify the simulated data and the measured data. The results show that the proposed algorithms can effectively and quickly identify the type of abnormal disturbance in traction power supply system and further evaluate its severity. © 2023 Electric Power Automation Equipment Press. All rights reserved.

引用

页码：217 / 224

页数：7

共 22 条

[1]

ZHAO Chaopeng, Low-frequency voltage oscillation and mitigation of catenary system in Xuzhou railway hub［J］, Journal of Electric Power Science and Technology, 31, 2, pp. 89-96, (2016)

[2]

23, 6, pp. 35-37, (2012)

[3]

TAO Haidong, HU Haitao, ZHU Xiaojuan, Et al., Mechanism on resonance instability of traction power supply system considering interaction between trains and networks［J］, Proceedings of the CSEE, 39, 8, pp. 2315-2324, (2019)

[4]

CHEN Huafeng, ZHANG Gexiang, Power quality disturbance identification using decision tree and support vector machine ［J］, Power System Technology, 37, 5, pp. 1272-1278, (2013)

[5]

JIA Junyi, Study on locomotive-grid electrical matching problem of Sichuan-Tibet railway and its monitoring algorithm ［D］, (2021)

[6]

WU Jianzhang, MEI Fei, PAN Yi, Et al., Novel detection method of power quality disturbance based on IEWT［J］, Electric Power Automation Equipment, 40, 6, pp. 142-151, (2020)

[7]

YI Huijuan, GAO Yunpeng, ZHU Yanqing, Et al., Recognition of composite power quality disturbance based on improved incomplete S transform and LOO-KELM algorithm［J］, Electric Power Automation Equipment, 42, 1, pp. 199-205, (2022)

[8]

Yan WANG, Qunzhan LI, Fulin ZHOU, Et al., A new method with Hilbert transform and slip-SVD-based noise-suppression algorithm for noisy power quality monitoring［J］, IEEE Transactions on Instrumentation and Measurement, 68, 4, pp. 987-1001, (2019)

[9]

WANG Jidong, ZHANG Di, Power quality disturbance classification method based on side-output fusion convolutional neural network［J］, Electric Power Automation Equipment, 41, 11, pp. 107-112, (2021)

[10]

YANG Qiuyu, RUAN Jiangjun, HUANG Daochun, Et al., Over-travel detection of electrical contact for high-voltage circuit breaker based on improved HHT and SVM［J］, Electric Power Automation Equipment, 39, 1, pp. 198-204, (2019)

← 1 2 3 →