Estimation of uncertainty intervals in the identification of electromechanical modes from ambiente data

Abstract

This paper discusses the estimation of uncertainty intervals associated with the electromechanical modes identified from ambient data resulting from random load switching throughout th 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 demostrated. The results of the presented method are compared with respect to the ones obtained from a Monte-Carlo type of simulation, showing its effectiveness in reducing the number of trials, which would be beneficial for on-line 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. The 2-area benchmark system, with different orders of the system identification model used (ARMA), is utilized to demostrate the effectiveness of the proposed methodology.