A new power quality disturbance detection method based on the improved LMD

被引:0
|
作者
Song, Haijun [1 ]
Huang, Chuanjin [1 ]
Liu, Hongchao [2 ]
Chen, Tiejun [3 ]
Li, Jingli [3 ]
Luo, Yong [3 ]
机构
[1] School of Engineering and Technology, Zhongzhou University, Zhengzhou 450044, Henan Province, China
[2] Hunan Electric Power Design Institute, Changsha 410007, Hunan Province, China
[3] Zhengzhou University, Zhengzhou 450001, Henan Province, China
关键词
Power quality - Quality control - Mathematical transformations;
D O I
10.13334/j.0258-8013.pcsee.2014.10.023
中图分类号
学科分类号
摘要
A new signal analysis method of non-linear, non-stationary-local mean decomposition (LMD) was introduced. The advantages and disadvantages of the application of LMD in the disturbance signal detection were analyzed. On this basis, the paper presented an improved local mean decomposition (ILMD) power quality disturbance detection and time-frequency analysis method. The method was composed of two parts: LMD and Hilbert transform. Firstly, to extract the product function (PF) of the signal based on LMD, and to obtain the instantaneous amplitude of the signal from the PF components of the amplitude modulation function. Then, to apply the Hilbert transform to obtain the instantaneous frequency of PF. The improved LMD method could effectively locate the beginning and ending time of the disturbances occurred and overcome the deficiencies of the LMD positioning capability. By using the method, the curves of the instantaneous amplitude had less distortion in wave head and that of the instantaneous frequency had shorter swings, and detection results for magnitude and frequency of disturbance signal had higher accuracy, compared with the HHT method. The simulation and experimental results of the switch voltage analysis of capacitor group from 500 kV transformer substation prove the feasibility and effectiveness of the proposed method. © 2014 Chin. Soc. for Elec. Eng.
引用
收藏
页码:1700 / 1708
相关论文
共 50 条
  • [21] Power quality disturbance detection based on IEWT
    Li, Ning
    Zhu, Longhui
    Li, Yixin
    ENERGY REPORTS, 2023, 9 : 512 - 521
  • [22] Improved Disturbance Detection Technique for Power-Quality Analysis
    Gomes Marques, Cristiano Augusto
    Ferreira, Danton Diego
    Freitas, Lucas Romero
    Duque, Carlos Augusto
    Ribeiro, Moises Vidal
    IEEE TRANSACTIONS ON POWER DELIVERY, 2011, 26 (02) : 1286 - 1287
  • [23] A method of locating transient disturbance of power quality based on morphological edge detection
    Ouyang, Sen
    Huang, Runhong
    Dianwang Jishu/Power System Technology, 2012, 36 (04): : 63 - 67
  • [24] Research for the mixed disturbance detection of power system using LMD algorithm
    1600, International Frequency Sensor Association, 46 Thorny Vineway, Toronto, ON M2J 4J2, Canada (161):
  • [25] A new method for measurement and classification of power quality disturbance
    School of Electrical Engineering, Wuhan University, Wuhan 430072, China
    不详
    Zhongguo Dianji Gongcheng Xuebao, 31 (125-133):
  • [26] Transient power quality disturbance denoising and detection based on improved iterative adaptive kernel regression
    Wang, Yan
    Li, Qunzhan
    Zhou, Fulin
    JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2019, 7 (03) : 644 - 657
  • [27] Power Quality Disturbance Detection Based on Improved Wavelet Threshold Function and Variational Mode Decomposition
    Xu C.
    Gu T.
    Gao Y.
    Wu C.
    Long Q.
    Zhou J.
    Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences, 2020, 47 (06): : 77 - 86
  • [28] Transient power quality disturbance denoising and detection based on improved iterative adaptive kernel regression
    Yan WANG
    Qunzhan LI
    Fulin ZHOU
    JournalofModernPowerSystemsandCleanEnergy, 2019, 7 (03) : 644 - 657
  • [29] An Improved Power Quality Disturbance Detection Using Deep Learning Approach
    Sekar, Kavaskar
    Kanagarathinam, Karthick
    Subramanian, Sendilkumar
    Venugopal, Ellappan
    Udayakumar, C.
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022
  • [30] Power Quality Disturbance Identification Method Based on Improved CEEMDAN-HT-ELM Model
    Liu, Ke
    Han, Jun
    Chen, Song
    Ruan, Liang
    Liu, Yutong
    Wang, Yang
    PROCESSES, 2025, 13 (01)