Abstract
Using time-synchronized phasor measurements, a new signal processing approach for estimating the electromechanical mode shape properties from ambient signals is proposed. In this method, Bayesian information criterion and the ARMA(2n,2n – 1) modeling procedure are first used to automatically select the optimal model order, and the auto regressive moving averaging models are built based on ambient data, then the low-frequency oscillation modal frequency and damping ratio are identified. Next, Prony models of ambient signals are presented, and the mode shape information of multiple dominant interarea oscillation modes are simultaneously estimated. The advantages of the new ARMA-P method are demonstrated by its applications in both a simulation system and measured data from China Southern Power Grid.
Highlights
Modal frequency, modal damping ratio, mode shape magnitude, and mode shape angle are the key parameters describing the electromechanical modal properties of a power system [1]
Based on its theoretical analysis basis, the approach is applied to a simulation system and measured data from China Southern Power Grid
The results demonstrate that the optimal model order can be selected automatically and efficiently using Bayesian information criterion (BIC) and the ARMA(2n,2n – 1) modeling procedure
Summary
Modal damping ratio, mode shape magnitude, and mode shape angle are the key parameters describing the electromechanical modal properties of a power system [1]. Near real-time knowledge of mode shape characteristics provide critical information for the optimization of generators and/or load shedding, in order to improve the damping of the dangerously low-damped modes of power systems. In order to identify the low-frequency oscillation mode shape properties based on ambient signals, a new multiple modes estimation method called the auto regressive moving averaging-Prony (ARMA-P) is proposed. The results demonstrate that the new ARMA-P method can effectively estimate the mode shape properties from ambient data. Measurement-based electromechanical mode estimation assumes that the power system is in the steady-state condition described by (1). The linear transformation defined in (3) is applied to the system (1), and the system state xi is calculated, shown in (4)
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