Advanced Kalman Filter for Real-Time Responsiveness in Complex Systems
- UNC-Chapel Hill/University of Central Florida
- UNC-Chapel Hill/Virginia Tech
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 developed 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 looked at complex system issues including severe nonlinearity of system equations, discontinuities caused by system controls and network switches, sparse measurements in space and time, and real-time requirements of power grid operations. We sought to bridge the disciplinary boundaries between Computer Science and Power Systems Engineering, by introducing methods that leverage both existing and new techniques. While our methods were developed in the context of electrical power systems, they should generalize to other large-scale scientific and engineering applications.
- Research Organization:
- The University of North Carolina at Chapel Hill, Department of Computer Science, CB 3175, Chapel Hill, NC 27599-3175
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- Contributing Organization:
- The University of North Carolina at Chapel Hill
- DOE Contract Number:
- SC0002271
- OSTI ID:
- 1133510
- Report Number(s):
- DOE-UNC-SC0002271-FINAL
- Country of Publication:
- United States
- Language:
- English
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