The enhancement of the reliability, security, and resiliency of electric power systems depends on the availability of fast, accurate, and robust dynamic state estimators. These estimators should be robust to gross errors on the measurements and the model parameter values while providing good state estimates even in the presence of large dynamical system model uncertainties and non-Gaussian thick-tailed process and observation noises. It turns out that the current Kalman filter-based dynamic state estimators given in the literature suffer from several important shortcomings, precluding them from being adopted by power utilities for practical applications. To be specific, they cannot handle (i) dynamic model uncertainty and parameter errors; (ii) non-Gaussian process and observation noise of the system nonlinear dynamic models; (iii) any type of outliers that are induced by impulsive measurement and system process noises, or incorrect system parameter values, to cite a few; and (iv) all types of cyber attacks. To address these challenges, this project will resort to both robust statistical theory and robust control theory to develop a general theoretical framework for robust dynamic state and parameter estimation. This new general framework will provide reliable real-time state and parameter estimates for power system monitoring, control, protection, and security analysis. In addition, it will contribute to the next generation of online state estimators with synchrophasor measurements and the redesign of robust detectors against cyber attacks. The project also contains an integrated educational agenda for K-12 students, undergraduates and graduate students who are interested in the STEM (Science Technology Engineering and Mathematics) area.
This project will pioneer a general theoretical framework that integrates both robust statistical theory and robust control theory for robust dynamic state and parameter estimation of a cyber-physical system. Specifically, the generalized maximum-likelihood-type (GM)-estimator, the unscented Kalman filter, and the H-infinity filter will be integrated into a unified framework to yield various centralized and decentralized robust dynamic state estimators. These new estimators will be able to handle large system uncertainties as well as suppress three types of outliers while achieving good statistical efficiency under a broad range of non-Gaussian process and observation noise. The three types of outliers, including observation, innovation, and structural outliers are caused by either an unreliable dynamical model or real-time synchrophasor measurements with data quality issues, which are commonly seen in the power system. Furthermore, the theories of robust statistics will be extended to structured nonlinear regression models. That is, the theory of breakdown point in linear structured regression will be extended to nonlinear dynamical models characterized by sparse Jacobian matrices, which is precisely the case for power systems. To this end, the global and local breakdown points of all the proposed methods will be investigated. Finally, the developed methods will be implemented and tested on two practical power systems, including the Southern Brazil power system and the Dominion Virginia Power 500-KV transmission system, which is observed through a set of redundant real-time synchrophasor measurements.
