Study On Power System Dynamic State Estimation Using Hybrid Measurements

Power system state estimation is vital for energy managementsystem (EMS), which can provide data for operators to analyze andcontrol the system.Wide area measurement system (WAMS) equipped with phasormeasurement units (PMU) can provide rich and high precision phasormeasurements, which meets the needs of dynamic state estimation. Howto use the data under hybrid measurements becomes a matter of greatpublic interest of the electricity companies.The background, significance and purpose of the thesis were firstlyintroduced. Then the models of the components in power system werediscussed, followed by a brief introduction to static state estimation. Thenthe algorithm basis of dynamic state estimation was analyzed andperformance index frequently used were proposed.The differences between WAMS and SCADA were carefullyanalyzed, which includes the delay of SCADA, the principle of PMU andthe problems happened when fusing the two types of measurements inpower system.A new model that tries to combine the static and the dynamicestimation method is proposed. The fast decoupled method is applied tothe static state estimation for SCADA data first. Then the estimation ofthis step combined with the voltage phasor and branch current phasorsmeasured by PMU forms the data for the linear dynamic state estimation.The quality and accuracy of the fast-decoupled method are ensured.IEEE14and IEEE30bus system are used as test systems and thesimulation results demonstrate that the proposed state estimation methodimproves the precision and gets better behavior as compared with otherstate estimators. A sequential robust dynamic state estimation is proposed based onthe analysis of the SCADA estimation time window of themixed-measure system. Firstly, the algorithm fusions PMU measurementssampling at the same time and then SCADA measurements are taken intoestimation in sequential of arrival time. Then the cost function iscalculated. A set of SCADA/PMU measurements is formed and used forstate estimation via extended Kalman filter if the cost function exceeds athreshold The algorithm can make good use of utilization of theestimation center, reduce the impact of measurement outliers on the stateestimation and improve state estimation real-time and accuracy. Finally,simulations on an IEEE14and IEEE30bus system demonstrate theeffectiveness and superiority of the proposed algorithm.Finally an adaptive sequential robust dynamic state estimation isproposed. Model parameters are on-line identified and modified toimprove the estimation performance under different scenarios such asnormal operation, bad measurements， sudden load change or drasticgeneration variation. Simulations on an IEEE14bus test systemdemonstrate the feasibility and validity of the proposed algorithm.