Identification of Interarea Modes From Ambient Data of Phasor Measurement Units Using an Autoregressive Exogenous Model

Abstract

Various modal identification methods for interarea modes have been developed to improve identification accuracy by overcoming the measurement noise in ambient data of phasor measurement units (PMUs). In this study, a modal identification method that is insensitive to measurement noise is proposed by introducing bandpass filters, which extract a modal signal from an autocorrelation function of PMU ambient data. The bandwidth of the filters is set to be adequately narrow such that the noise can be rejected sufficiently. To reduce the computational burden of the proposed method, the filters are designed by transforming a reference lowpass filter. An autoregressive exogenous (ARX) model of interarea modes is applied to the extracted modal signal. The parameters of the ARX model are estimated for modal identification via the least squares method. The identification accuracy of the proposed method is compared with those of conventional modified extended Yule Walker and discrete Fourier transform methods with respect to the signal-to-noise ratio and modal damping by using synthetic ambient data. Finally, the feasibility of the proposed method is demonstrated by identifying interarea modes of Kundur’s two-area four-machine system and two real power systems in South Korea.

Keywords

• Ambient data
• autocorrelation function
• autoregressive exogenous model
• bandpass filter
• impulse function
• interarea oscillations