Core Concepts
Various techniques are compared for identifying low-frequency oscillations in power systems, proving reliability in model parameter identification.
Abstract
Abstract introduces techniques for identifying low-frequency oscillations in power systems.
Methods like Fourier transform, Prony's method, Matrix Pencil Analysis, S-transform, Global Wavelet Spectrum, and Hilbert Huang transform are compared.
Results show consistency in identifying dominant oscillation modes.
Practical applications and numerical results are presented.
Conclusion highlights the efficiency of the proposed methods in estimating oscillatory modes accurately.
Stats
"0.2 Hz is the major mode shared by all the monitoring locations." - Reference [1]
Quotes
"The signal processing based techniques discussed in this paper provides satisfactory performance in detecting the low frequency modes."