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Recognition of Post-contingency Dynamic Vulnerability Regions: Towards Smart Grids

Abstract:

This paper presents a novel approach for determining post-contingency dynamic vulnerability regions (DVRs), oriented to assess vulnerability in real time as part of Smart Grid applications. Based on the probabilistic models of input parameters, such as load variation and the occurrence of contingencies, Monte Carlo-type simulation is performed to iteratively evaluate the system time domain responses. The dynamic probabilistic attributes are then analyzed using time series data mining techniques, namely Multichannel Singular Spectrum Analysis (MSSA), and Principal Component Analysis (PCA), in order to recognize the system DVRs based on the patterns associated to three different short-term stability phenomena. The vulnerability criterion consists in the possibility of some N-1 contingencies driving the system to further undesirable events (i.e. N-2 contingencies), which could be considered as the beginning of …