This research focuses on understanding noisy nonlinear dynamical systems using multidisciplinary approaches from advanced measurement capabilities, modeling, and simulations. The unique features of the problem are interactions between information collection, information fusion and information assimilation. The subtleties of the interaction between these effects will lead to new and novel analytical techniques. The approach is to (1) develop novel data-driven lower-order modeling and multi-scale nonlinear filtering frameworks using probability-based decision-making approaches for state estimation of the power system; (2) develop new algorithms and tools for the distributed collection, sharing, mining and harnessing of data for cooperative health monitoring and vulnerability assessment of power systems in real time; and (3) enhance overall power system dynamic performance and stability through novel data fusion techniques and particle methods which are specifically adapted to the complexities of the underlying dynamical system.
The daily operation of interconnected electric power systems must guard against failures due to natural disasters, human attack, and failures due to aging and unexpected conditions. At the heart of this effort is huge data collection, analysis, and control strategies spanning time scales from microseconds to minutes and hours. Through the creation of new techniques this project will advance our understanding of the critical operational issues of the next-generation power system and will lead to smarter power grids to provide better electric energy security, efficiency, and sustainability.
Through this grant we will foster intensive training of engineering undergraduate students, developing their motivation to pursue graduate studies in this multidisciplinary area. The PIs anticipate a direct scientific benefit in training researchers who are well-versed in engineering, modeling, and applied mathematics. The Co-PI and his group have developed Applets designed for education of K-12 students in how power systems operate. An integral part of the final outcome of the proposed work will be to provide the community a predictive tool that will enhance visual understanding of the effect of noise and uncertainty on power system operations.
