Comparative study on power quality disturbance magnitude characterization

被引:0
|
作者
Wang, ZQ [1 ]
Zhu, SZ [1 ]
机构
[1] Tsing Hua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
关键词
magnitude characteristic; power quality; power quality disturbance; digital signal processing;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Power quality disturbances (PQD) are normally monitored by dedicated PQ devices. The devices capture disturbances' waveform in real-time. Magnitude is accepted as a significant index for detection, general classification and later assessment analysis. To choose suitable way of magnitude characterization is a fundamental work of PQD measuring and monitoring. This study presents three different ways, RMS voltage, peak voltage and fundamental voltage component, to determine magnitude. The algorithms of the three approaches implemented in Matlab are introduced. The algorithms are FFT-based and wavelet transformation (WT) based. A voltage sag benchmark in a radial test system is used to verify and validate the study. The depth-duration characterization of voltage sags is illustrated. The most appropriate approach is suggested at the end of the paper according to the practical monitoring or,measuring requirements. The research result is applied to a new PQ monitor with DSP infrastructure being developed by us. The proper window length selection and technique to avoid oscillation is also discussed in the rest part of the paper. The information obtained from the magnitude characterization can be furthered to extract features for PQD detection and classification.
引用
收藏
页码:106 / 111
页数:6
相关论文
共 50 条
  • [1] Power quality disturbance magnitude characterization using wavelet transformation analysis part 2 - Application
    Zin, A. A. Mohd.
    Goh, H.H.
    Lo, K.L.
    International Journal of Power and Energy Systems, 2008, 28 (02): : 190 - 201
  • [2] Comparisons on ways of magnitude characterization of power quality disturbances
    Wang, ZQ
    Zhou, SZ
    Guo, YJ
    LESCOPE'02: 2002 LARGE ENGINEERINGS SYSTEMS CONFERENCE ON POWER ENGINEERING, CONFERENCE PROCEEDINGS, 2002, : 178 - 183
  • [3] 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
  • [4] Comparative Performance of Different Wavelets in Power Quality Disturbance Detection and Quantification
    Divya, S.
    Rao, K. Uma
    2016 SECOND INTERNATIONAL CONFERENCE ON COGNITIVE COMPUTING AND INFORMATION PROCESSING (CCIP), 2016,
  • [5] Study of Power Quality Disturbance Location Based On MUDW
    Zhang, Hong
    Lei, Zhiguo
    Zhang, Yunbo
    Sun, Yuexing
    Zhang, Benfa
    2018 CHINESE AUTOMATION CONGRESS (CAC), 2018, : 97 - 100
  • [6] A study on flicker evaluation considering power quality disturbance of power system
    Jung, S. B.
    Kirn, J. C.
    Choi, K. H.
    2007 IEEE POWER ENGINEERING SOCIETY GENERAL MEETING, VOLS 1-10, 2007, : 3534 - 3539
  • [7] Comparative study of decay ratios of disturbance-rejection magnitude optimum method for PI controllers
    Lumbar, Satja
    Vrancic, Damir
    Strmnik, Stanko
    ISA TRANSACTIONS, 2008, 47 (01) : 94 - 100
  • [8] Experimental Study on Power Quality Disturbance Tolerance and Performance of SSTS
    Huo Xianxu
    Lv Jinbing
    Guo Bingwen
    Li Kangcheng
    2021 POWER SYSTEM AND GREEN ENERGY CONFERENCE (PSGEC), 2021, : 558 - 564
  • [9] Wavelet based detection of power quality disturbance - A case study
    Christy, J.
    Jeno Vedamani, X.
    Karthikeyan, S.
    2011 - International Conference on Signal Processing, Communication, Computing and Networking Technologies, ICSCCN-2011, 2011, : 157 - 162
  • [10] Study of the Bus Voltage Magnitude to Monitor Power Quality in the Distribution System
    Men, Christopher R.
    Dow, Douglas E.
    Ghanavati, Afsaneh
    2022 IEEE ELECTRICAL POWER AND ENERGY CONFERENCE (EPEC), 2022, : 18 - 23