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Data‐mining‐based approach for predicting the power system post‐contingency dynamic vulnerability status

Abstract:

This paper proposes a data‐mining‐based approach for predicting the power system post‐contingency vulnerability status in real time. To this aim, the system dynamic vulnerability regions (DVRs) are first determined by applying singular value decomposition, by means of empirical orthogonal functions (EOFs), to a post‐contingency data base obtained from phasor measurement units (PMUs) adequately located throughout the system. In this way, the obtained pattern vectors (EOF scores) allow mapping the DVRs within the coordinate system formed by the set of EOFs, which permits revealing the main patterns immersed in the collected PMU signals. The database along with the DVRs enable the definition, training, and identification of a support vector classifier (SVC) which is employed to predict the post‐contingency vulnerability status as regards three short‐term stability phenomena, that is: transient stability …