Synchrophasor measurement-based correlation approach for dominant mode identification in bulk power systems

This study proposes a novel synchrophasor measurement-based correlation approach to identify the dominant oscillation modes in bulk power systems. In the proposed approach, the reference channel (RC) of cross-correlation (CC) is optimally selected based on residue analysis. With the selected RC, the CC of field-measurement data is formed to extract the free-decay system responses. Then, eigensystem realisation algorithm (ERA) is applied to the extracted responses to estimate the system state-space model. A practical model order selection strategy for ERA is proposed to determine the system model order. Further, cross-coherence spectrum is employed to distinguish the dominant modes from the eigenvalues of the estimated state-space model. The proposed method can achieve accurate and robust solutions in the presence of different levels of errors and noises in measurement data. The effectiveness of the proposed method has been verified in a 16-generator, 68-bus test system as well as the China Southern Power Grid system.

[1]  D. Trudnowski,et al.  A stepwise regression method for estimating dominant electromechanical modes , 2012, 2012 IEEE Power and Energy Society General Meeting.

[2]  Tao Jiang,et al.  Stochastic subspace identification-based approach for tracking inter-area oscillatory modes in bulk power system utilising synchrophasor measurements , 2015 .

[3]  C. Rehtanz,et al.  Wide-Area Robust Coordination Approach of HVDC and FACTS Controllers for Damping Multiple Interarea Oscillations , 2012, IEEE Transactions on Power Delivery.

[4]  P. Kundur,et al.  Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions , 2004, IEEE Transactions on Power Systems.

[5]  J. C. Peng,et al.  Enhancing Kalman Filter for Tracking Ringdown Electromechanical Oscillations , 2012, IEEE Transactions on Power Systems.

[6]  Ning Zhou,et al.  Initial results in power system identification from injected probing signals using a subspace method , 2006, IEEE Transactions on Power Systems.

[7]  Oriol Gomis-Bellmunt,et al.  Input–output signal selection for damping of power system oscillations using wind power plants , 2014 .

[8]  Ning Zhou,et al.  Robust RLS Methods for Online Estimation of Power System Electromechanical Modes , 2007, IEEE Transactions on Power Systems.

[9]  A. R. Messina,et al.  Nonlinear, non-stationary analysis of interarea oscillations via Hilbert spectral analysis , 2006, IEEE Transactions on Power Systems.

[10]  Jer-Nan Juang,et al.  An Eigensystem Realization Algorithm in Frequency Domain for Modal Parameter Identification , 1986 .

[11]  C.W. Taylor,et al.  The anatomy of a power grid blackout - Root causes and dynamics of recent major blackouts , 2006, IEEE Power and Energy Magazine.

[12]  J. F. Hauer,et al.  Initial results in Prony analysis of power system response signals , 1990 .

[13]  I. C. Decker,et al.  Wide-Area Measurements-Based Two-Level Control Design Considering Signal Transmission Delay , 2009, IEEE Transactions on Power Systems.

[14]  C. W. Taylor,et al.  Model validation for the August 10, 1996 WSCC system outage , 1999 .

[15]  I. Erlich,et al.  Wavelet-Based Analysis of Power System Low-Frequency Electromechanical Oscillations , 2011, IEEE Transactions on Power Systems.

[16]  W. Mittelstadt,et al.  Electromechanical Mode Online Estimation Using Regularized Robust RLS Methods , 2008, IEEE Transactions on Power Systems.

[17]  Gerard Ledwich,et al.  Mode matching pursuit for estimating dominant modes in bulk power grid , 2014 .

[18]  Jukka Turunen,et al.  Modal analysis of power systems through natural excitation technique , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[19]  I Kamwa,et al.  Robust Detection and Analysis of Power System Oscillations Using the Teager-Kaiser Energy Operator , 2011, IEEE Transactions on Power Systems.