Open AccessDiscrete Fourier transform‐based parametric modal identification from ambient data of the power system frequency
Author(s) -
Hwang Jin Kwon,
Liu Yilu
Publication year - 2016
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2015.0699
Subject(s) - autocorrelation technique , autocorrelation , discrete fourier transform (general) , mathematics , autocorrelation matrix , laplace transform , parametric statistics , fourier transform , algorithm , modal , function (biology) , z transform , mathematical analysis , fractional fourier transform , fourier analysis , statistics , polymer chemistry , biology , chemistry , evolutionary biology
This study proposes a discrete Fourier transform (DFT)‐based parametric identification method by using a difference sequence between two sets of frequency data. An autocorrelation function of the frequency difference sequence can be represented as a linear combination of exponentially decaying sinusoidal functions, whose natural frequencies and damping ratios are equal to those of interarea modes. These modal parameters are calculated from the Laplace transform coefficients of the autocorrelation function. The coefficients are estimated by curve‐fitting the DFT values of the autocorrelation function. The proposed method is compared with the modified extended Yule Walker method through simulations on signal‐to‐noise ratio. Finally, the feasibility of the proposed method is shown by identifying real power systems from frequency data of the frequency monitoring network system.