Confidence Intervals Estimation in the Identification of Electromechanical Modes From Ambient Noise

This paper discusses the estimation of uncertainty intervals associated with the electromechanical modes identified from ambient data resulting from random load switching throughout the day in power systems. A connection between the second order statistical properties, including confidence intervals, of the identified electromechanical modes and the variance of the parameters of a selected linear model is demonstrated. The results of the presented method are compared with respect to the ones obtained from a Monte Carlo technique, showing its effectiveness in reducing the number of trials, which would be beneficial for online power system monitoring, as it can decrease the number of samples, thus ensuring that the system dynamics would not change significantly over the monitoring time window, and yielding more dependable results. Two test cases, namely, the two-area benchmark system and the IEEE 14-bus system, with different orders of the system identification model used, are utilized to demonstrate the effectiveness of the proposed methodology.

[1]  D. Trudnowski.  Order reduction of large-scale linear oscillatory system models , 1994 .

[2]  H. Ghasemi,et al.  Oscillatory stability limit prediction using stochastic subspace identification , 2006, IEEE Transactions on Power Systems.

[3]  H. Ghasemi,et al.  On-Line Damping Torque Estimation and Oscillatory Stability Margin Prediction , 2007, IEEE Transactions on Power Systems.

[4]  Hassan Ghasemi.  On-line Monitoring and Oscillatory Stability Margin Prediction in Power Systems Based on System Identification , 2006 .

[5]  J. F. Hauer,et al.  Application of Prony analysis to the determination of modal content and equivalent models for measured power system response , 1991 .

[6]  P. Kundur,et al.  Power system stability and control , 1994 .

[7]  V. Kroupa,et al.  Digital spectral analysis , 1983, Proceedings of the IEEE.

[8]  J.W. Pierre,et al.  Use of least-mean squares (LMS) adaptive filtering technique for estimating low-frequency electromechanical modes in power systems , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[9]  Poul Henning Kirkegaard,et al.  Identification of Civil Engineering Structures using Vector ARMA Models , 1998 .

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

[11]  Donald A. Pierre,et al.  A comparison of several system identification methods with application to power systems , 1993, Proceedings of 36th Midwest Symposium on Circuits and Systems.

[12]  Hassan Ghasemi,et al.  Estimation of uncertainty intervals in the identification of electromechanical modes from ambiente data , 2006 .

[13]  D.J. Trudnowski,et al.  Use of ARMA block processing for estimating stationary low-frequency electromechanical modes of power systems , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[14]  Steven Kay,et al.  Modern Spectral Estimation: Theory and Application , 1988 .

[15]  J.W. Pierre,et al.  Adaptive Filtering Techniques for Estimating Electromechanical Modes in Power Systems , 2007, 2007 IEEE Power Engineering Society General Meeting.

[16]  J.W. Pierre,et al.  Using Adaptive Step-Size Least Mean Squares (ASLMS) for Estimating Low-Frequency Electromechanical Modes in Power Systems , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.

[17]  C. K. Yuen,et al.  Digital spectral analysis , 1979 .

[18]  Joshua R. Smith,et al.  Transfer function identification in power system applications , 1993 .

[19]  J.W. Pierre,et al.  Bootstrap-based confidence interval estimates for electromechanical modes from multiple output analysis of measured ambient data , 2005, IEEE Transactions on Power Systems.

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

[21]  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.