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In recent years, some Smart Grid applications have been designed in order to perform timely Self-Healing and adaptive reconfiguration actions based on system-wide analysis, with the objective of reducing the risk of power system blackouts. Real time dynamic vulnerability assessment (DVA) has to be done in order to decide and coordinate the appropriate corrective control actions, depending on the event evolution. This paper presents a novel approach for carrying out real time DVA, focused on Transient Stability Assessment (TSA), based on some time series data mining techniques (Multichannel Singular Spectrum Analysis MSSA, and Principal Component Analysis PCA), and a machine learning tool (Support Vector Machine Classifier SVM-C). In addition, a general overview of the state of the art of the methods to perform vulnerability assessment, with emphasis in the potential use of PMUs for post-contingency DVA, is described. The developed methodology is tested in the IEEE 39 bus New England test system, where the simulated cause of vulnerability is transient instability. The results show that time series data mining tools are useful to find hidden patterns in electric signals, and SVM-C can use those patterns for effectively classifying the system vulnerability status.

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Abstract-Vulnerability assessment is one of the main tasks in a Self-Healing Grid structure, since it has the function of detecting the necessity of performing global control actions in real time. Due to the short-time requirements of real time applications, the eligible vulnerability assessment methods have to consider the improvement of calculation time. Although there are several methods capable of performing quick assessment, these techniques are not fast enough to analyze real large power systems in real time. Based on the fact that vulnerability begins to develop in specific regions of the system exhibiting coherent dynamics, large interconnected power systems can be reduced through dynamic equivalence in order to reduce the calculation time. A dynamic equivalent should provide simplicity and accuracy sufficient for system dynamic simulation studies. Since the parameters of the dynamic equivalent cannot be easily derived from the mathematical models of generators and their control systems, numerical identification methods are needed. Such an identification task can be tackled as an optimization problem. This paper introduces a novel heuristic optimization algorithm, namely, the Mean-Variance Mapping Optimization (MVMO), which provides excellent performance in terms of convergence behavior and accuracy of the identified parameters. The identification procedure and the level of accuracy that can be reached are demonstrated using the Ecuadorian-Colombian interconnected system in order to obtain a dynamic equivalent representing the Colombian grid.