Research Summary of Power Quality Disturbance Detection and Classification Recognition Method Based on Transform Domain

被引：1
作者：
Li-Ping, Qu [1 ]
Chang-Long, He [2 ]
Jie, Zhang [2 ]
机构：
[1] Beihua Univ, Engn Training Ctr, Jilin, Jilin, Peoples R China
[2] Beihua Univ, Coll Elect & Informat Engn, Jilin, Jilin, Peoples R China
来源：
2020 19TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS FOR BUSINESS ENGINEERING AND SCIENCE (DCABES 2020) | 2020年
关键词：
Transform domain; Power quality; Wavelet transform; Extreme learning machine; Short-time Fourier transform;
D O I：
10.1109/DCABES50732.2020.00022
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
With the diversification of power connection forms and increasing types of loads, the power quality of the power system is deteriorating. Various indicators of power quality are essential for the normal operation of the power grid, especially the increasing harmonic pollution caused by various nonlinear loads. Therefore, power quality disturbance detection and classification recognition is the key to improve power quality. This article combines the current domestic and foreign power quality related standards, summarizes the feature extraction of electric energy quality disturbance based on transform domain, meanwhile recognize and classify the extracted feature vectors.
引用
收藏
页码：50 / 53
页数：4
相关论文
共 50 条
  • [31] RETRACTED: The Detection of Power Quality Hybrid Disturbance Based on S Transform (Retracted Article)
    Zhao Wen-yuan
    2010 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2010,
  • [32] Research on Power Quality Disturbance Signal Classification Based on Random Matrix Theory
    Liu, Keyan
    Jia, Dongli
    He, Kaiyuan
    Zhao, Tingting
    Zhao, Fengzhan
    DATA SCIENCE, PT II, 2017, 728 : 365 - 376
  • [33] A CNN-based Power Quality Disturbance Decomposition and Classification System Using Wavelet Transform
    Dai, Siting
    Wu, Xuyan
    PROCEEDINGS OF 2021 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INFORMATION SYSTEMS (ICAIIS '21), 2021,
  • [34] A new power quality disturbance detection method based on the improved LMD
    Song, Haijun
    Huang, Chuanjin
    Liu, Hongchao
    Chen, Tiejun
    Li, Jingli
    Luo, Yong
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2014, 34 (10): : 1700 - 1708
  • [35] Power quality disturbance classification based on time-frequency domain multi-feature and decision tree
    Zhao, Wenjing
    Shang, Liqun
    Sun, Jinfan
    PROTECTION AND CONTROL OF MODERN POWER SYSTEMS, 2019, 4 (01)
  • [36] Rule based system for power quality disturbance classification incorporating S-transform features
    Salem, Mohammad E.
    Mohamed, Azah
    Samad, Salina Abdul
    EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (04) : 3229 - 3235
  • [37] A Comparative Study of Signal Processing and Pattern Recognition Approach for Power Quality Disturbance Classification
    Panigrahi, B. K.
    Sinha, Sunil Kumar
    Mohapatra, Ankita
    Dash, Priyadarshini
    Mallick, Manas Kumar
    IETE JOURNAL OF RESEARCH, 2011, 57 (01) : 5 - 11
  • [38] A New Method for Identification and Classification of Power Quality Disturbance Based on Modified Kaiser Window Fast S-transform and LightGBM
    Yin B.
    Chen Q.
    Li B.
    Zuo L.
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2021, 41 (24): : 8372 - 8383
  • [39] Multiresolution S-transform-based fuzzy recognition system for power quality events
    Chilukuri, MV
    Dash, PK
    IEEE TRANSACTIONS ON POWER DELIVERY, 2004, 19 (01) : 323 - 330
  • [40] Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection
    Chen, Sihan
    Li, Ziche
    Pan, Guobing
    Xu, Fang
    ELECTRONICS, 2022, 11 (02)
12345