Identification of inter-area oscillations using Zolotarev filter bank with eigen realization algorithm

Inter-area oscillations can be identified by analyzing the signals obtained from Phasor Measurement Units (PMUs) dispersed across the power network. In this paper, signals obtained from PMU are decomposed into monocomponent signals by a filter bank based on Zolotarev polynomials. A narrow bandwidth of 0.1 Hz is designed in the range of 0 to 1 Hz using Zolotarev polynomial based filter bank. Modal frequency and damping are estimated from the decomposed monocomponent signals through Eigen Realization Algorithm (ERA). The proposed method is demonstrated on two-area test system and the simulation results are presented.

[1]  D. R. Ostojic Spectral monitoring of power system dynamic performances , 1993 .

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

[3]  Pavel Zahradnik,et al.  Perfect Decomposition Narrow-Band FIR Filter Banks , 2012, IEEE Transactions on Circuits and Systems II: Express Briefs.

[4]  J. W. Pierre,et al.  Use of ARMA Block Processing for Estimating Stationary Low-Frequency Electromechanical Modes of Power Systems , 2002, IEEE Power Engineering Review.

[5]  Xiaorong Xie,et al.  Small Signal Stability Assessment with Online Eigenvalue Identification Based on Wide-area Measurement System , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

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

[7]  Xiaorong Xie,et al.  Dynamic tracking of low-frequency oscillations with improved Prony method in wide-area measurement system , 2004, IEEE Power Engineering Society General Meeting, 2004..

[8]  Daniel J. Trudnowski,et al.  Initial results in electromechanical mode identification from ambient data , 1997 .

[9]  Ning Zhou,et al.  Performance of Three Mode-Meter Block-Processing Algorithms for Automated Dynamic Stability Assessment , 2008, IEEE Transactions on Power Systems.

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

[11]  Jer-Nan Juang,et al.  An eigensystem realization algorithm for modal parameter identification and model reduction. [control systems design for large space structures] , 1985 .

[12]  A.R. Messina,et al.  A Refined Hilbert–Huang Transform With Applications to Interarea Oscillation Monitoring , 2009, IEEE Transactions on Power Systems.

[13]  M. G. Lauby,et al.  A comprehensive computer program package for small signal stability analysis of power systems , 1990 .

[14]  Pavel Zahradnik,et al.  Fast analytical design algorithms for FIR notch filters , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[15]  Rolf Unbehauen,et al.  Zolotarev polynomials and optimal FIR filters , 1999, IEEE Trans. Signal Process..

[16]  James D. McCalley,et al.  Damping controller design for power system oscillations using global signals , 1996 .

[17]  A.G. Phadke,et al.  Synchronized phasor measurements in power systems , 1993, IEEE Computer Applications in Power.

[18]  Joe H. Chow,et al.  Performance comparison of three identification methods for the analysis of electromechanical oscillations , 1999 .

[19]  J. F. Hauer,et al.  Making Prony analysis more accurate using multiple signals , 1999 .

[20]  Innocent Kamwa,et al.  A minimal realization approach to reduced-order modelling and modal analysis for power system response signals , 1993 .