• DocumentCode
    3503944
  • Title

    Inter-harmonics analysis method based on particle swarm optimization algorithm

  • Author

    Guo Tian-dong ; Wang Jing-yu ; Guo Song-lin

  • Author_Institution
    Coll. of Electr. & Inf. Eng., Heilongjiang Inst. of Sci. & Technol., Harbin, China
  • Volume
    02
  • fYear
    2013
  • fDate
    16-18 Aug. 2013
  • Firstpage
    1296
  • Lastpage
    1301
  • Abstract
    With the deterioration of harmonics pollution in power system, it is of great importance to accurately find out the harmonics component for the safe and economical operation of the power system. To effectively detect the harmonics and inter harmonics in electrical signals, an input matrix is built with series of time delay sampling signals. The waveform of each component can be obtained by independent component analysis (ICA) based on maximum likelihood estimation, and frequencies of the fundamental wave, harmonics and inter- harmonics in signal can be obtained also. Using these values as initial condition, particle swarm optimization (PSO) algorithm is run and the amplitude and phase of each component consisting of the signals are accurately computed. Matlab simulation results show that this method is more accurate to analysis inter-harmonic parameters, especially to the inter-harmonics between closer frequency, still be able to accurately analyze their parameters.
  • Keywords
    independent component analysis; maximum likelihood estimation; particle swarm optimisation; power electronics; Matlab simulation; deterioration; electrical signals; fundamental wave; harmonics pollution; independent component analysis; initial condition; input matrix; interharmonic parameters; interharmonics analysis method; maximum likelihood estimation; particle swarm optimization; time delay sampling signals; Algorithm design and analysis; Frequency estimation; Harmonic analysis; Optimization; Particle swarm optimization; Power system harmonics; Time-frequency analysis; Independent component analysis (ICA); Inter-harmonic; Matlab simulation; Maximum likelihood estimation; Particle swarm optimization (PSO);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Measurement, Information and Control (ICMIC), 2013 International Conference on
  • Conference_Location
    Harbin
  • Print_ISBN
    978-1-4799-1390-9
  • Type

    conf

  • DOI
    10.1109/MIC.2013.6758197
  • Filename
    6758197