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.

In this paper, a classifier based early warning system is designed, trained and tested based on time-series of Phasor Measurement Unit (PMU) measurements at all buses in a power system. The classifier is based on a novel combination of Graph Attention Networks and Long Short-Term memories, and is trained to label power system data in the form of captured windows of PMU measurements. These labels are then used to provide early warning for transient instability. The classifier is trained and tested data from simulations of the Nordic44 test system, and includes extensive topological variations under two different load levels. It is found that accurate early warnings can be provided, but the quality of prediction is highly dependent on specific power system characteristics, such as how quickly the power system responds to transient disturbances.

With decreasing inertia and short circuit ratios, the modern power grid is becoming more dynamic, increasing the need for dynamic state estimation (DSE). These DSE applications are dependent on data from the available data-sharing architectures, and are therefore challenged by the architectural limitations. This paper presents an analysis of how DSE is affected by these challenges, with a focus on incorrect topology and limited observability. Through a centralised Extended Kalman Filter (EKF) and Unscented Kalman filter (UKF) approach, DSE performance is evaluated using phasor measurement unit (PMU) and topology data, through a data-architecture abstraction. Both Kalman filters algorithms prove to be robust to temporary topology errors when estimating angles, while UKF outperforms EKF in terms of root-mean-square error (RMSE). Both are also capable of estimating complete phasors, angles and magnitudes, during high observability conditions. However, while phasor angles could be estimated during low observability conditions, the phasor magnitude could not be estimated by either approach.

Control room applications for multi-areas systems, e.g. oscillations detection, are dependent on models and measurements of neighbouring areas. Given inherent limitations in state-of-the-art data-sharing architectures, such dependencies can suffer from model imperfections and low observability. This paper presents a study on the use of the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) for centralised estimation of rotor angles, in support of applications such as oscillation detection. The study focused on the Kalman filter variants ability to overcome low observability and model imperfections, implemented on the Kundur two-area four-machine network and the Nordic-44 bus network. To study the effect of low observability and model imperfection limitations, the study included three different data sharing architectures as proposed by ENTSO-E. The simulated test cases demonstrated that the EKF is unable to capture the dynamics under low observability conditions during model imperfections, while UKF captures the dynamics for all presented cases.