A semi-distributed energy-based framework for the analysis and visualization of power system disturbances

Abstract In this paper, a semi-distributed tool for monitoring and analysis of power systems disturbances using wide-area sensors is proposed. The proposed framework can be used to integrate and process large amounts of multimodal, multi-type observational data from various monitoring technologies or data concentrators to assess the power system health in near-real time. First, a framework for fusing data from multiple sensors based on multivariate statistical tools is introduced and a model of the collected data is developed. Drawing on this model, a novel a data-driven strategy based on both, local and global energy metrics for the analysis of major system disturbances is proposed. The approach is tested using time domain simulations on the IEEE 118 bus system under various scenarios and different levels of observability of the system. The approach is complemented with a visualization technique to provide further means to analyze the system after a disturbance.

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