Wide-area power system monitoring research will help reduce power outages
CSL and ITI Professors Rakesh Bobba and Nitin Vaidya recently received a three-year, $400,000 grant from the National Science Foundation for research on developing algorithms and system software for wide-area monitoring of power systems. They will be working with teams at North Carolina State University and the Renaissance Computing Institute (RENCI) at the University of North Carolina at Chapel Hill on the collaborative research grant titled “Distributed Asynchronous Algorithms and Software Systems for Wide-Area Monitoring of Power Systems.”
Interest in wide-area view came to the forefront after the 2003 blackout in the Northeast where over 50 million people in the Northeastern and Midwestern states and parts of Canada were without power. At the time, it was the most widespread blackout in North American history, according to Scientific American.
Situation awareness through the use of Phasor Measurement Unit (PMUs) capabilities gained momentum as a result of the blackout. PMUs are used for this type of monitoring because they enable measurements from outside a control region to be combined with measurements from inside the region. However, as the number of PMUs increase, Independent System Operators (ISO) and utility companies are struggling to harvest, process and utilize the enormous amounts of real-time data being collected. Current centralized monitoring architectures are no longer sustainable, so the team is developing distributed algorithms and architectures for wide-area monitoring.
“Power systems are so interconnected now,” Bobba said. “Distributed algorithms and architectures will make monitoring and control more effective and enable operators to respond to disturbances faster than they were able to in the past. Security and reliability are important concerns for these distributed algorithms. Our role is to make the distributed monitoring algorithms secure and fault tolerant.”
The multi-institution team will be conducting a thorough analytical investigation of the stability, convergence and robustness properties of PMU-based real-time algorithms while in a decentralized computational framework. Additionally, they will be implementing, validating and testing the algorithms under different types of faults and malicious attacks.