Adaptive tracking of ambient system oscillations by nonstationary RLS techniques

Moreno, I.; Messina, A.R.

doi:10.1109/pes.2011.6039658

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Traditionally, monitoring of electromechanical oscillations has been based on nonparametric spectral estimation methods [4]- [5]. Parametric mode estimation algorithms have also been developed which provide numerical estimates of the mode's damping ratio and frequency [6]- [9]. These studies have resulted in an improved understanding of the mechanisms and origins of the measured ambient noise [5].…”

Section: Introductionmentioning

confidence: 99%

Estimation of modal parameters from ambient data using singular spectrum analysis

Cabrera

Ramos

Barocio

2014

2014 IEEE PES Transmission &Amp; Distribution Conference and Exposition - Latin America (PES T&D-La)

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The development of advanced signal processing algorithms to extract modal information from ambient system oscillations has acquired a great deal of interest in recent years. Much of this effort has been directed towards the problem of feature extraction, which involves estimating, identifying and extracting oscillatory phenomena embedded in highly noisy random processes.In this paper, a statistical framework for analyzing ambient signals from power systems is proposed. The method combines singular spectral analysis with a random decrement technique to identify and estimate power system oscillatory phenomena from ambient system oscillations. The paper also describes the experience with the application of this technique to quantify modal information on both synthetic and ambient data.Index Terms--Electromechanical oscillations, ambient signals, singular spectrum analysis, random decrement.I.

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