Browsing by Author "Ghasemi, Hassan"

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    Confidence Intervals Estimation in the Identification of Electromechanical Modes From Ambient Noise
    (Institute of Electrical and Electronics Engineers (IEEE), 2008-04-22) Ghasemi, Hassan; Canizares, Claudio A.
    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.
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    Oscillatory Stability Limit Prediction Using Stochastic Subspace Identification
    (Institute of Electrical and Electronics Engineers (IEEE), 2006-05-01) Ghasemi, Hassan; Canizares, Claudio A.; Moshref, Ali
    Determining stability limits and maximum loading margins in a power system is important and can be of significant help for system operators for preventing stability problems. In this paper, stochastic subspace identification is employed to extract the critical mode(s) from the measured ambient noise without requiring artificial disturbances (e.g., line outages, generator tripping, and adding/removing loads), so that the identified critical mode may be used as an online index to predict the closest oscillatory instability. The proposed index is not only independent of system models and truly represents the actual system, but it is also computationally efficient. The application of the proposed index to several realistic test systems is examined using a transient stability program and PSCAD/EMTDC, which has detailed models that can capture the full dynamic response of the system. The results show the feasibility of using the proposed identification technique and index for online detection of proximity to oscillatory stability problems.