Feature Analysis and Extraction Method of Power Grid Frequency Signal Based on Measured Data

被引：0
|
作者：
Liu H. [1 ]
Shang J. [1 ]
Bi T. [1 ]
Li Y. [2 ]
机构：
[1] State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing
[2] Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2023年 / 47卷 / 10期
基金：
中国国家自然科学基金;
关键词：
convolution neural network (CNN); frequency feature extraction; information source location recognition; Pearson correlation coefficient; variational mode decomposition (VMD);
D O I：
10.7500/AEPS20220518001
中图分类号：
学科分类号：
摘要：
With the large-scale integration of renewable energy, the rotational inertia, the frequency characteristics, and the support capacity of different regions in the power system have changed greatly. The extraction and identification of frequency features can provide a basis for the recognition of power grid characteristics and further analysis of inertia evaluation, frequency control, and network security. Based on a large number of measured data from the source-grid-load full-view synchronized measurement system, this paper analyzes the clustering phenomenon related to the frequency and the AC power grid structure, and proposes a frequency spatial correlation identification method based on the Pearson correlation coefficient. A “frequency fingerprint”extraction method based on convolutional neural networks is proposed to extract the high-latitude features of the power grid characteristics in the frequency domain. Furthermore, the measured frequency signals of ten cities, such as Beijing and Changzhi, are tested and analyzed, and the identification accuracy is given, which verifies the effectiveness of the proposed method and provides a basis for the subsequent frequency characteristic analysis, inertia evaluation and cyber attack identification of the power system. © 2023 Automation of Electric Power Systems Press. All rights reserved.
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页码：135 / 144
页数：9
相关论文
共 23 条
  • [1] HUANG Linbin, XIN Huanhai, HUANG Wei, Et al., Quantified analysis method of frequency response characteristics for power systems with virtual inertia［J］, Automation of Electric Power Systems, 42, 8, pp. 31-38, (2018)
  • [2] ZHANG Hengxu, LIU Yutian, Quantitative description of space-time distribution features of dynamic frequency responses［J］, Proceedings of the CSEE, 29, 7, pp. 64-70, (2009)
  • [3] HU Pengfei, ZHU Naixuan, JIANG Daozhuo, Et al., Research progress and prospects of key technologies of flexible interconnected smart distribution network［J］, Automation of Electric Power Systems, 45, 8, pp. 2-12, (2021)
  • [4] JIA Hongjie, MU Yunfei, HOU Kai, Et al., Morphology evolution and operation regulation of urban energy system from perspective of energy transition［J］, Automation of Electric Power Systems, 45, 16, pp. 49-62, (2021)
  • [5] XU Xiaoyuan, WANG Han, YAN Zheng, Et al., Overview of power system uncertainty and its solutions under energy transition ［J］, Automation of Electric Power Systems, 45, 16, pp. 2-13, (2021)
  • [6] CUI Y，, BAI F F，, LIU Y L，, Et al., Spatio-temporal characterization of synchrophasor data against spoofing attacks in smart grids［J］, IEEE Transactions on Smart Grid, 10, 5, pp. 5807-5818, (2019)
  • [7] YAO W X, ZHAO J C，, TILL M J, Et al., Source location identification of distribution-level electric network frequency signals at multiple geographic scales［J］, IEEE Access, 5, pp. 11166-11175, (2017)
  • [8] LI Yiyan, HU Rongxing, SONG Lidong, Et al., Application of machine learning in field of smart power distribution and utilization：overview of engineering practice in North America ［J］, Automation of Electric Power Systems, 45, 16, pp. 99-113, (2021)
  • [9] LI Zikang, LIU Hao, BI Tianshu, Et al., A power system disturbance classification method based on enhanced denoising autoencoder and random forest［J］, Modern Electric Power, 39, 2, pp. 127-136, (2022)
  • [10] LIU S Y，, YOU S T，, YIN H，, Et al., Model-free data authentication for cyber security in power systems［J］, IEEE Transactions on Smart Grid, 11, 5, pp. 4565-4568, (2020)
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