Award Abstract #0540237
DDDAS-TMRP: Data-Driven Power System Operations

NSF Org:	CNS Division of Computer and Network Systems
Initial Amendment Date:	September 14, 2005
Latest Amendment Date:	September 28, 2009
Award Number:	0540237
Award Instrument:	Standard Grant
Program Manager:	Krishna Kant CNS Division of Computer and Network Systems CSE Directorate for Computer & Information Science & Engineering
Start Date:	January 1, 2006
Expires:	December 31, 2009 (Estimated)
Awarded Amount to Date:	$146000
Investigator(s):	Peter Sauer sauer@ece.uiuc.edu (Principal Investigator) M. Pai (Co-Principal Investigator) N. Sri Namachchivaya (Co-Principal Investigator) Thomas Overbye (Co-Principal Investigator)
Sponsor:	University of Illinois at Urbana-Champaign SUITE A CHAMPAIGN, IL 61820 217/333-2187
NSF Program(s):	ITR-DYNAMIC DATA DRIV APP SYS, COMPUTER SYSTEMS
Field Application(s):	0000912 Computer Science
Program Reference Code(s):	HPCC, 9251, 9218, 9178
Program Element Code(s):	7581, 7354

ABSTRACT

The main objective of this research is to develop new algorithms and tools for the distributed collection, sharing, mining and harnessing of data for cooperative health monitoring and vulnerability assessment of power systems in real time. Fundamental concepts and technology for achieving this objective exist today in the areas of power systems, system theory and computer science, but harnessing them to achieve this vision will require the significant multidisciplinary concerted effort proposed here. The project aims at making fundamental contributions to the necessarily

multidisciplinary systems required for data utilization in monitoring and control of interconnected

engineering systems. The application that this proposal focuses on is the creation of new data-driven tools for electric power system operation and control. The applications systems include traditional SCADA systems as well as the emerging Phasor Measurement Unit (PMU) data concentrators. This effort will focus on five specific task areas: Distributed agent-based system monitoring; Pattern recognition and change detection; Dynamic analysis of signatures for early warning; Data-driven, low-order modeling for security assessment; Automated visualization for power system health monitoring. This work will provide a broader impact in several areas. First, the results in data manipulation, visualization, and processing will have application in other domains. Any complex system that relies heavily on measurements for monitoring and control shares the same need to extract key information from massive amounts of data received over wide areas and time scales. Second, several of the investigators work very closely with the electric power industry through the Power Systems Engineering Research Center (PSERC), an NSF Industry/University Cooperative Research Center designed to stimulate collaboration among 13 universities and 35 companies. Results from this project will naturally influence that collaboration in the form of seminars, tutorials, and interaction with industry.

Please report errors in award information by writing to: awardsearch@nsf.gov.