2013•Applied Mechanics and MaterialsOpen access

Distinction of the Property of Low Frequency Oscillation Based on ARMA Mode Identification

Lian Tu, Di Chen Liu, Qing Fen Liao, Fei Fei Dong, Xingpei Ji, Chun Li Song, Zhen Shan Zhu

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Abstract

Aiming at how to solve the question of quantitatively distinguish negative damping low frequency oscillation and forced power oscillation, the difference in frequency and damping ratio between the two kinds of oscillations have been discovered through mechanism analysis, and a new quantitative distinction criterion for the property of the low frequency is proposed. The oscillation data is windowing identified by ARMA model based on weighted recursive least squares algorithm dynamically, and low frequency type can be distinguished according to the changes of oscillation frequency and damping ratio during the oscillation. The simulative results have shown that the method is feasible and effective.

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What this paper is about

Aiming at how to solve the question of quantitatively distinguish negative damping low frequency oscillation and forced power oscillation, the difference in frequency and damping ratio between the two kinds of oscillations have been discovered through mechanism analysis, and a new quantitative distinction criterion for the property of the low frequency is proposed. The oscillation data is windowing identified by ARMA model based on weighted recursive least squares algorithm dynamically, and low frequency type can be distinguished according to the changes of oscillation frequency and damping ratio during the oscillation. The simulative results have shown that the method is feasible and effective.

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Available abstract

Aiming at how to solve the question of quantitatively distinguish negative damping low frequency oscillation and forced power oscillation, the difference in frequency and damping ratio between the two kinds of oscillations have been discovered through mechanism analysis, and a new quantitative distinction criterion for the property of the low frequency is proposed. The oscillation data is windowing identified by ARMA model based on weighted recursive least squares algorithm dynamically, and low frequency type can be distinguished according to the changes of oscillation frequency and damping ratio during the oscillation. The simulative results have shown that the method is feasible and effective.

Key concepts: Low-frequency oscillation, Oscillation (cell signaling), Control theory (sociology), Damping ratio, Property (philosophy), Mode (computer interface), Low frequency, Mathematics

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