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A Data-driven Situation Awareness Method Based on Random Matrix for Future Grids


Data-driven methodologies are more suitable for a complex grid with readily accessible data when tasked with situation awareness. However, it is a challenge to turn the massive data, especially those with some spatial or temporal errors, into the driving force within tolerable cost of resources such as time and computation. This paper, based on random matrix theory (RMT), outlines a novel data-driven methodology. 1) Background information and previous work are reviewed. 2) Related to the methodology, the technical route and applied framework, data-proceeding and each procedure, evaluation system and related indicator set, and the advantages over classical methodologies are studied. Moreover, we make a comparison with the data-driven methodology based on Principal Component Analysis (PCA). 3) Related functions, including anomaly detection, spectrum test, correlation analysis, fault diagnosis and location, statistical indicator system and its visualization (i.e. 3D power map), are developed. This methodology gains insight into the large-scale interconnected grid in a more precise and natural way, it is model free requiring no knowledge about the physical model parameters. The methodology, in a flexible and holistic way, processes massive data in the form of large random matrix to depict a global but not a local picture of the system. Meanwhile, the large data dimension $N$ and the large time span $T$, from the spatial aspect and the temporal aspect respectively, benefit the engineering performance of the proposed methodology, for this paper, the robustness against unsynchronized data is highlighted.