Show simple item record

dc.contributor.authorZhao, Junboen
dc.date.accessioned2018-05-31T08:00:28Zen
dc.date.available2018-05-31T08:00:28Zen
dc.date.issued2018-05-30en
dc.identifier.othervt_gsexam:15186en
dc.identifier.urihttp://hdl.handle.net/10919/83423en
dc.description.abstractThe 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) three types of outliers; and (iv) all types of cyber attacks. 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. To address these challenges, we have pioneered a general theoretical framework that advances both robust statistics 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 (UKF), and the H-infinity filter are integrated into a unified framework to yield various centralized and decentralized robust dynamic state estimators. These new estimators include the GM-iterated extended Kalman filter (GM-IEKF), the GM-UKF, the H-infinity UKF and the robust H-infinity UKF. The GM-IEKF is able to handle observation and innovation outliers but its statistical efficiency is low in the presence of non-Gaussian system process and measurement noise. The GM-UKF addresses this issue and achieves a high statistical efficiency under a broad range of non-Gaussian process and observation noise while maintaining the robustness to observation and innovation outliers. A reformulation of the GM-UKF with multiple hypothesis testing further enables it to handle structural outliers. However, the GM-UKF may yield biased state estimates in presence of large system uncertainties. To this end, the H-infinity UKF that relies on robust control theory is proposed. It is shown that H-infinity is able to bound the system uncertainties but lacks of robustness to outliers and non-Gaussian noise. Finally, the robust H-infinity filter framework is proposed that leverages the H-infinity criterion to bound system uncertainties while relying on the robustness of GM-estimator to filter out non-Gaussian noise and suppress outliers. Furthermore, these new robust estimators are applied for system bus frequency monitoring and control and synchronous generator model parameter calibration. Case studies of several different IEEE standard systems show the efficiency and robustness of the proposed estimators.en
dc.format.mediumETDen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectKalman filteren
dc.subjectRobust statisticsen
dc.subjectPower system state estimationen
dc.subjectDynamic state estimationen
dc.subjectUnscented transformationen
dc.subjectRobust control theoryen
dc.subjectEstimation theoryen
dc.subjectPower system dynamics and controlen
dc.subjectOutliersen
dc.subjectCyber attacksen
dc.subjectPhasor measurement unitsen
dc.titleA Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Controlen
dc.typeDissertationen
dc.contributor.departmentElectrical Engineeringen
dc.description.degreePh. D.en
thesis.degree.namePh. D.en
thesis.degree.leveldoctoralen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineElectrical Engineeringen
dc.contributor.committeechairMili, Lamine M.en
dc.contributor.committeememberRahman, Saifuren
dc.contributor.committeememberSandu, Adrianen
dc.contributor.committeememberWang, Yue J.en
dc.contributor.committeememberKekatos, Vasileiosen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record