A Bootstrap Method for Statistical Power System Mode Estimation and Probing Signal Selection

Near real-time measurement-based electromechanical-mode estimation offers considerable potential for many future power-system operation and control strategies. Recent research has investigated the use of low-level pseudo random noise (PRN) probing signals injected into power systems to estimate the low-frequency electromechanical modes. Because of the random nature of a power system, estimating the modes from a single probing experiment is very difficult. Ideally, one would use a Monte-Carlo approach with multiple independent probing experiments resulting in a mode estimation distribution. Then, one could state that the mode is within a region of the complex plane. Unfortunately, conducting multiple probing experiments is prohibitive for most power-system applications. This paper presents a methodology for estimating the mode distribution based on one probing test using a Bootstrap algorithm. The proposed method is applied to both simulation data and actual-system measurement data to illustrate its performance and application. It is demonstrated that the method can provide valuable information to PRN tests and guide future PRN probing signal design and selection

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

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

[3]  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.

[4]  R. F.,et al.  Mathematical Statistics , 1944, Nature.

[5]  Innocent Kamwa,et al.  Low-order black-box models for control system design in large power systems , 1995 .

[6]  Boualem Boashash,et al.  The bootstrap and its application in signal processing , 1998, IEEE Signal Process. Mag..

[7]  Marco Lovera,et al.  Bootstrap-based estimates of uncertainty in subspace identification methods , 2000, Autom..

[8]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[9]  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).

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

[11]  B. Efron Bootstrap Methods: Another Look at the Jackknife , 1979 .

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

[13]  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.