CHAPEL HILL, NC – Yufeng Xin, a senior researcher in RENCI’s networking research group, continues to work with Aranya Chakrabortty, assistant professor of electrical and computer engineering at NC State University, and researchers at the University of Illinois in Urbana-Champaign on a project to develop new algorithms for controlling and monitoring large distributed power systems.
The research team headed by Chakrabrotty received a new $1 million, three-year grant through the National Science Foundation’s Cyber Physical System (CPS) program late last year. The funding will be used to explore using cloud computing to analyze smart grid data from thousands of sensors, called Phasor Measurement Units, or PMUs. The PMUs are distributed across the transmission grid and connect a wide range of energy generating plants, including wind turbines and solar panels.
According to Xin, the communication and information processing architecture for efficient and reliable monitoring and control of power grids has changed in recent years from more centralized power stations to distributed systems. With more heterogeneous power sources and more sensors to monitor power data, comes the need to analyze that data more efficiently and in real time.
RENCI’s ExoGENI test bed, a nationwide test bed for advanced networking experiments and scientific research using networked cloud computing, is used to link the real-time sensor data to on-demand virtual computing resources at ExoGENI nodes throughout the U.S. The process could someday evolve into the standard method for monitoring and troubleshooting smart grids: sensors collect as many as 120 data points per second, high-speed networks with guaranteed bandwidth connect the data to computing resources at many sites and provision a slice of virtual machines, or VMs. The VMs then run algorithms to analyze and visualize the data in real time.
Power grid control systems that fail to take advantage of innovations in virtual IT infrastructure such as the ExoGENI framework simply can’t process as much data as quickly, says Xin. The bottom line, he said, is making the data easier to access and analyzing it quickly so that problems can be spotted before they become critical.
For more on this research, see the RENCI story on the research team’s US Ignite award or see this NC State news release. For more on ExoGENI, see the ExoGENI website.
