ABSTRACT
As deploying the smart grid infrastructure to power systems, new challenges rise in handling extremely large datasets for better decision making. It is critical to utilize such large datasets in a power system for better operation, control, and protection. In particular, we will focus on discussing data types and acquisition, data management and how to integrate big data for feature extraction, systematic integration, and contingency analysis of power systems.
The term big data usually refers to large datasets that are difficult to store, process, and analyze with traditional database and analysis tools. As we are building the smart grid infrastructure, we are facing new challenges in analyzing big data for more accurate and robust operation, control, and protection. There are three characteristics of big data in power systems as indicated in [9]: large volume, high velocity, and increasing variety. While building smart grid infrastructure, many metering devices have been introduced to the power systems, which dramatically increase the data volume. In addition, many phasor measurement units (PMU) or synchrophasors have been employed into power systems, which significantly increase the data velocity. The PMU device measures the electrical waves on an electricity grid, using a common time source for synchronization. Time synchronization
