Enhanced wavelet-based method for modal identification from power system ringdowns

Abstract

In this paper, a method to the analysis of lowfrequency electromechanical oscillations (LFEOs) based on continuous wavelet transform (CWT) is presented. The complex Morlet CWT is employed to decouple power system ringdown signals into single oscillatory modes (OMs) in order to estimate their frequencies, damping ratios, relative amplitudes and phase shifts as well as to detect nonstationarities. End-effects of CWT may cause inaccurate estimation of relative amplitudes and phase shifts. To make up for this drawback, an optimization problem is formulated in a least-square sense. The method is applied to synthetic and simulated ringdown signals and results are contrasted to results from Prony analysis.Index Terms--Modal identification, small signal stability, power system ringdowns, continuous wavelet transform, optimization.

Syncrophasor measurement based small signal stability assesment using FFT-CWT approach in Japan-Campus-WAMS

“…Thus, the CWT possesses localization properties in both time and frequency domains and consequently provides valuable information about f(t) at different levels of resolution and measures the similarity between and each son wavelet [11].…”

“…The Eigen values of the i th mode expressed as O i =D i + jZ i . f c is the wavelet central frequency parameter and f b is a bandwidth parameter that controls the shape of the wavelet [11].…”

“…VALIDATION OF THE RESULT Fig.4 shows a system which consist of two identical area interconnected by a weak tie-line. The model is similar to the system in example-12.6 page-813 in [11]. A simulation of small signal stability performance is carried out by applying an auto reclose action of the circuit breaker at the end one of a tie line from bus 7 to bus 8 in Fig.4.…”

Wavelet-demodulation-method based out of step detection and damping estimation in Japan campus warns

“…Thus, the CWT possesses localization properties in both time and frequency domains and consequently provides valuable information about f(t) at different levels of resolution and measures the similarity between and each son wavelet [5].…”

“…According to those literatures the complex Morlet wavelet would be appropriate for the analysis of ringdown signals due to its capabilities in time-frequency localization for analytical signals. According to [5], the CWT is capable of analyzing data in a multi-resolution domain which means it can automatically filter out the noise from f(t) and thus no additional filters are needed.…”