Model order sensitivity in ARMA-based electromechanical mode estimation algorithms under ambient power system conditions

This paper presents the results of a model order sensitivity study performed on ARMA-based electromechanical mode estimation algorithms under ambient power system conditions. Both single- and multichannel versions of the Yule-Walker (YW) and Least Squares (LS) approaches are considered along with a Recursive Maximum Likelihood (RML) estimator. An extensive Monte Carlo study was performed using a reduced-order model of the Western United States power system, where it was seen that while all of the algorithms had similar accuracy in their best case scenarios, the YW algorithms were far less sensitive to changes in model order. These results are supported with a study using data measured from the actual WECC system. Additionally, the study revealed a numerical instability inherent to the RML approach that has identified an important area of future work.

[1]  Alex Simpkins,et al.  System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf] , 2012, IEEE Robotics & Automation Magazine.

[2]  Luigi Vanfretti,et al.  Power-System Ambient-Mode Estimation Considering Spectral Load Properties , 2014, IEEE Transactions on Power Systems.

[3]  Dmitry Kosterev,et al.  PDCI damping control analysis for the western North American power system , 2013, 2013 IEEE Power & Energy Society General Meeting.

[4]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[5]  D. Wilkes,et al.  An improved Hessian matrix for recursive maximum likelihood ARMA estimation , 1992 .

[6]  Luigi Vanfretti,et al.  Power-System Ambient-Mode Estimation Considering Spectral Load Properties , 2014 .

[7]  Petre Stoica,et al.  Spectral Analysis of Signals , 2009 .

[8]  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.

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

[10]  J. W. Pierre,et al.  A Recursive Maximum Likelihood Estimator for the Online Estimation of Electromechanical Modes With Error Bounds , 2013, IEEE Transactions on Power Systems.

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

[12]  John W. Pierre,et al.  Estimating electromechanical modes and mode shapes using the multichannel ARMAX model , 2013, IEEE Transactions on Power Systems.