Performance Evaluation of Hybrid State Estimation Using Real TSO Data Sources

To gain insight into grid dynamics, many transmission system operators are in the process of deploying phasor measurement units. However, observing all dynamic events which occur can still be challenging due to limited deployment and communication malfunctions, i.e. unmetered buses and missing data. The issue has incentivized the research into hybrid state estimation - where classic static state estimation is integrated with dynamic state estimation. The development of such techniques is still ongoing, and there are few real-world deployments. Using weighted least squares, the extended Kalman filter and the unscented Kalman filter, this paper presents a hybrid state estimation technique validated on data from a transmission system operator, where the PMUs are sparsely placed, implying low observability. The results demonstrate that the estimation error is low while exposed to dynamic phenomena, even with sparse PMU deployment.