A Comparative Assessment of Two Techniques for Modal Identification From Power System Measurements

Abstract

Power system oscillatory behavior can be analyzed in terms of modes, expressed as exponentially modulated sinusoids, exhibited in signals measured on the system. These signals are driven by the behavior of a large, nonlinear, time-variant system. In this paper, two modal identification methods are examined comparatively: Prony analysis and a method based on the Hilbert transform. Considerable structural differences exist between the two methods. Prony analysis yields modes which are directly expressed as exponentially modulated sinusoids, whereas the Hilbert method provides a more general solution. Synthetic and measured signals are used in the comparison. Some general conclusions are drawn from the analysis of several signals, including two sets of measured field data.Index Terms-Dynamics, empirical mode decomposition (EMD), Hilbert transforms, Hilbert-Huang method, interarea oscillations, power system dynamic stability, power system measurements, power systems, prony analysis.

Disaggregation of the electric loads of small customers through the application of the Hilbert transform

“…Our research is based on the Hilbert transform (HT), a mathematical tool that has been used in power systems to solve other problems (Browne et al 2008;Senroy et al 2007). HT has not been used previously for the analysis of pulse/square load waveforms.…”

Power system low frequency oscillation monitoring and analysis based on multi-signal online identification

“…Therefore, the size of the system model is limited [5]. Measured data analysis (MDA) such as the Fourier transform, the Hilbert-Huang transform and the Prony analysis [6,7] are easier to apply and consequently have been widely used.…”

“…The studies on low frequency oscillations are generally limited to oscillation frequency identification [6,9]. The oscillation frequency is computed according to the measured data, and the corresponding oscillation mode and mode shape are then determined from eigenanalysis results.…”

A Refined Hilbert–Huang Transform With Applications to Interarea Oscillation Monitoring

“…Recently, nonlinear and nonstationary analysis techniques based on the Hilbert-Huang transform (HHT) [20] have been used to analyze data from nonlinear and nonstationary processes [6], [21], [22]. The method has been applied to many important problems in various fields including medical [23], geophysics [24], and power engineering [25].…”