Data Analysis for Real Time Identification of Grid Disruptions

The U.S. electric power system comprises multiple distinct interconnections of generators, high voltage transmission systems, and local distribution systems that maintain a continuous balance between generation and load with impressive levels of efficiency and reliability. This critical infrastructure has served the nation remarkably well, but is likely to see more changes over the next decade than it has seen over the past century. In particular, the widespread deployment of renewable generation, smart-grid controls, energy storage, and new conducting materials will require fundamental changes in grid planning and the way we run the power grid. Two challenges in the realization of the smart grid technology are the ability to visualize the deluge of expected data streams for global situational awareness; as well as the ability to detect disruptive and classify such events from spatially-distributed high-speed power system frequency measurements. One element of smart grid technology is the installation of a wide-area frequency measurement system on the electric poles in the streets for conditions monitoring of the distribution lines. This would provide frequency measurements about the status of the electric grid and possible information about impending problems before they start compounding and cascading. The ability to monitor the distribution lines is just one facet of proposed smart grid technology. Other elements include the installation of advanced devices such as smart meters, the automation of transmission lines, the integration of renewable energy technologies such as solar and wind, and the advancement of plug-in hybrid electric vehicle technology. This chapter describes recent advancements in the area of intelligent data analysis for real time detection of disruptive events from power system frequency data collected using an existing internet-based frequency monitoring network (FNET), which is a precursor for future smart-grid systems