ABSTRACT

To make well-informed decisions, power system operators need a robust accurate real-time estimator of the state of the operational conditions of the power grid. Up to the present time, conventional static state estimators have been widely deployed in utility control centers to improve the estimation accuracy and expand the monitoring areas. However, these estimators are no longer sufficient for monitoring the modern power grid, which is experiencing increasing uncertainty and variation driven by the high penetration of intermittent renewable (especially solar and wind) generation. In fact, conventional static state estimation methods for power grids often fail in providing any useful information during transmission-line tripping and cascading grid failures when the power system rapidly changes, and state estimation results are crucially needed. There is a technical gap in modeling a complex system, which is not fully understood, and whose behaviors can change rapidly. To bridge the gap, the project team will develop a data-fusion framework for an integrated dynamic state estimator (iDSE) that can not only estimate current operational conditions but also predict their future trends, and quantify their uncertainty. Because the framework addresses the fundamental issue in the situational awareness of a complex system, the research results will shed light on that research challenge in other complex infrastructures, which are time-varying, and with high uncertainty. The project team will disseminate the new theory and methods to industry and academia, train college students, and historically underrepresented middle/high-school students. Thus the project will increase diversity and improve the preparation of future power system engineers so that the power grid can be modernized to host more renewable generation.


The goal of the project is to develop a data-fusion framework for an integrated dynamic state estimator (iDSE) to estimate and predict power system states by integrating signal processing theory and statistical inference theory. Encouraged by preliminary results that multiple-hypothesis filtering algorithms can track rapid variation in states, and the observation that belief function theory can more efficiently handle the uncertainty from incomplete and conflicting information than Bayesian probability theory, the new iDSEs will be created by integrating belief function theory and multiple-hypothesis testing with multiple models to assimilate heterogeneous data and gain the following three capabilities: (1) Leveraging dynamical models together with static power flow models, the new iDSEs will achieve additional robustness through increased spatial and temporal redundancy; (2) Leveraging the capability of belief function theory to explicitly model incomplete and conflicting information, the new iDSEs will efficiently quantify and mitigate the negative impacts of both aleatory and epistemic uncertainty inherent in the power system; (3) Leveraging multiple dissimilar estimation criteria and models, the new iDSEs will effectively deal with quick dynamical changes in the power system and predict future states using multiple-hypothesis testing. It is expected that the new iDSE will significantly increase the situational awareness of an operator and lay the groundwork for transforming state estimation and power system operations from the current static paradigm into a future dynamic paradigm.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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