Data will become a strategic resource and even prime driving force for future grid. Essentially, rather than massive data themselves, we are much more interested in the potential contained in the data. In other words, how to mine the value from the 4Vs data (data with features of volume, variety, velocity, and veracity) within tolerant resources (time, hardware, human, etc.) is the key challenge. This chapter studies the methodology of applying big data analytics to power grids. First, the definition of big data and random matrix theories (RMTs), as well as related system mapping framework and data processing methods are introduced as foundations. Especially, some mathematical contents, such as random matrix models (RMMs), probability in high dimension, and linear eigenvalue statistics (LES), are discussed in detail. Then, a series of functions related to situation awareness (SA) of power grids, including early event detection (EED), fault diagnosis and location, correlation analysis, high-dimensional indicator system and its visualization (i.e., auxiliary 3D power-map), are developed as concrete applications. In this way, a typical data-driven methodology, mainly based on RMT, is proposed to cognize power grids. Three main procedures are essential: (1) big data model-to build the RMMs with raw data; (2) big data analysis-to conduct high-dimensional analyses to construct the indicator system via statistical transformations; and (3) engineering interpretation-to visualize and interpret the statistical results to human beings. This methodology is a more precise and natural way to gain insight into the large-scale interconnected systems. Furthermore, the indicator system will build a new epistemology to reveal the physical systems; it will open a new ear for the SA.
Big data analysis of power grid from random matrix theory, Page 1 of 2
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