Abstract.The most critical element of the nation's energy infrastructure is our electricity generation, transmission, and distribution system known as the "power grid." Computer simulation is an effective tool that can be used to identify vulnerabilities and predict the system response for various contingencies. However, because the power grid is a very large-scale nonlinear system, such studies are presently conducted "open loop" using predicted loading conditions months in advance and, due to uncertainties in model parameters, the results do not provide grid operators with accurate "real time" information that can be used to avoid major blackouts such as were experienced on the East Coast in August of 2003. However, the paradigm of Dynamic Data-Driven Applications Systems (DDDAS) provides a fundamentally new framework to rethink the problem of power grid simulation. In DDDAS, simulations and field data become a symbiotic feedback control system and this is refreshingly different from conventional power grid simulation approaches in which data inputs are generally fixed when the simulation is launched. The objective of the research described herein was to utilize the paradigm of DDDAS to develop a marriage between sensing, visualization, and modelling for large-scale simulation with an immediate impact on the power grid. Our research has focused on methodological innovations and advances in sensor systems, mathematical algorithms, and power grid simulation, security, and visualization approaches necessary to achieve a meaningful large-scale real-time simulation that can have a significant impact on reducing the likelihood of major blackouts.

Detecting and aligning structured signals such as point grids plays a fundamental role in many signal processing applications. Joint determination of non-grid points and estimation of non-linear spatial distortions applied to the grid is a key challenge for grid alignment. This paper proposes a candidate solution. The method described herein starts from a small nearly regular region found in the point set and then expands the list of candidate points included in the grid. The proposed method was tested on geometrically transformed point sets and sets of locations derived from imagery of 3D prints. It is shown that a low-complexity grid alignment method can nonetheless achieve high grid alignment accuracy.

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A scanning tunneling microscope (STM) uses a piezoelectric actuator to perform constant-velocity scanning motion. Many feedback strategies have been proposed, but their achievable scan rate is substantially limited by the turnaround transients in the scan path. Therefore, a robust time-optimal command shaping technique with an iterative search procedure is introduced in this paper to improve the scan speed of piezoactuators, and is applicable to a general class of systems without rigid-body mode. Furthermore, a time-energy-optimal formulation is presented to reduce the in-maneuver oscillation. The hysteresis nonlinearity of piezoactuators is compensated using the proposed continuous numerical inversion algorithm. Finally, the closed-loop simulation shows the performance robustness in the presence of hysteresis cancellation error and natural frequency perturbation.

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