Advanced Kalman Filter for Real-Time Responsiveness in Complex Systems

Principal Investigator:Greg Welch
Funding Agency: Department of Energy
Agency Number:DE-SC0002271

Abstract
Complex engineering systems pose fundamental challenges in real-time operations and control because they are highly dynamic systems consisting of a large number of elements with severe nonlinearities and discontinuities. Today’s tools for real-time complex system operations are mostly based on steady state models, unable to capture the dynamic nature and too slow to prevent system failures. We propose the development of advanced Kalman filtering techniques and the formulation of dynamic state estimation using Kalman filtering techniques to capture complex system dynamics in aiding real-time operations and control. In this work, we are going to look at complex system issues including severe nonlinearity of system equations, discontinuities caused by system controls and network switchings, sparse measurements in space and time, and real-time requirements of power grid operations. The outcomes include an advanced Kalman filter technique with adaptive tuning and characterized convergency performance, a novel procedure of combined state estimation and model calibration, an approach of optimal sensor placement, and an implementation on high-performance computing platforms. Demonstration of applying the developed Kalman filter to large-scale dynamic systems will also be developed. This proposed research directly supports the ASCR’s advanced mathematics program in advancing mathematical formulations and applications in the area of control and optimization of complex system operations.