System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Higher dimensional consensus algorithms in sensor networks
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Distributed sensor localization in random environments using minimal number of anchor nodes
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Risk assessment of malicious attacks against power systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Distributing the Kalman Filter for Large-Scale Systems
IEEE Transactions on Signal Processing - Part I
Multi-scale Integration of Physics-Based and Data-Driven Models in Power Systems
ICCPS '12 Proceedings of the 2012 IEEE/ACM Third International Conference on Cyber-Physical Systems
Perceptual control architecture for cyber-physical systems in traffic incident management
Journal of Systems Architecture: the EUROMICRO Journal
Exploring demand flexibility in heterogeneous aggregators: An LMP-based pricing scheme
ACM Transactions on Embedded Computing Systems (TECS) - Special Section ESFH'12, ESTIMedia'11 and Regular Papers
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This paper proposes modeling the rapidly evolving energy systems as cyber-based physical systems. It introduces a novel cyber-based dynamical model whose mathematical description depends on the cyber technologies supporting the physical system. This paper discusses how such a model can be used to ensure full observability through a cooperative information exchange among its components; this is achieved without requiring local observability of the system components. This paper also shows how this cyber-physical model is used to develop interactive protocols between the controllers embedded within the system layers and the network operator. Our approach leads to a synergistic framework for model-based sensing and control of future energy systems. The newly introduced cyber-physical model has network structure-preserving properties that are key to effective distributed decision making. The aggregate load modeling that we develop using data mining techniques and novel sensing technologies facilitates operations of complex electric power systems.