Transient stability analysis is performed to assess the power system's condition after a severe contingency and is carried out using simulations. To adequately assess the system's transient stability, the correct dynamic models for the machines (i.e., generators, condensers, and motors) along with their dynamic parameters must be defined. The IEEE test systems contain the data required for steady-state studies. However, neither the dynamic model of the machines nor their specific parameters have been established for transient studies. As a result, there is a demand for test bed systems suitable for transient analysis. This paper defines dynamic machine models along with their parameters for each IEEE test bed system, thus producing full dynamic models for all test systems. It is important to mention that the parameters of the proposed dynamic models are based on typical data. The test systems are subjected to large disturbances, and a case study for each test system, which examines the frequency, angle, and voltage stability, is presented. Furthermore, the proposed dynamic IEEE test systems, implemented in PowerWorld, are available online.

The accurate knowledge of the transmission line parameters can be beneficial for several monitoring and control applications accommodated in the power system control center. Actually transmission line parameters are stored in the control center databases (assuming that they are time invariant); however, the databases might be obsolete and contain erroneous parameters. This paper proposes a methodology for identifying and estimating the erroneous transmission line parameters using measurements provided by phasor measurement units (PMUs) and estimated states provided by a state estimator. The main advantage of the proposed methodology is that for the identification of the erroneous transmission lines and the estimation of the line parameters only one PMU is required for the monitoring of the transmission line (i.e., capture measurements related to the transmission line). The proposed methodology is tested in the IEEE 14- and 118-bus systems.

The state estimator constitutes the cornerstone of the supervisory control and data acquisition system since it provides the power system operating situation in consecutive time intervals. Furthermore, the output of the state estimator is used by other tools responsible for the monitoring and control of the power system. Therefore, there is a need for the power system state estimator to be as accurate and reliable as possible. The main source of uncertainty that may deteriorate the accuracy of a weighted least squares (WLS) state estimator, provided that the network parameters are perfectly known, is the uncertainty that is encompassed in the measurements. It is well known that the measurement chain is not ideal and this information is passed to the state estimator through the measurement weights. In this paper, the effect of the measurement weights, which are calculated by considering both the standard uncertainties associated with the measurement devices and the instrument transformers, on the WLS state estimator is examined.

The parameters of the transmission lines are used in several monitoring and control tools of the power system control center. An accurate knowledge of the power system model (including transmission lines impedances) impacts positively the situational awareness of the power system operators. It also improves the protection and corrective measures that may be applied in case of a disturbance. Nevertheless, line parameters that are stored in the control center database are typically subjected to errors, and therefore it is necessary to regularly refine them. The presence of a phasor measurement unit (PMU) at both ends of the transmission line allows the calculation of the transmission line parameters using synchronized current and voltage phasor measurements. Although the use of PMU measurements is a straightforward way to calculate the transmission line parameters, the uncertainty of the synchronized phasor measurements should be incorporated to the calculated transmission line parameter values. In this paper, an analytical expression for the bounds of the calculated transmission line parameters from PMU measurements is derived, considering both instrument transformers and PMU errors. The calculated bounds of uncertainty are evaluated through Monte Carlo simulations, while the importance of knowing the bounds of the calculated line parameters is underlined through the derivation of power-voltage curves considering the lower and upper bounds of the parameters. The simulation results are obtained using the IEEE 14-bus system.

Power system operators are facing major challenges today to keep the system operating at the admissible limits. Recent blackouts demonstrated the need for a systematic study and design of a comprehensive system control strategy. Intentional Controlled Islanding (ICI) has been proposed as an effective corrective control action of final resort to save the system from a partial or a complete blackout. ICI limits the occurrence and consequences of blackouts by splitting the power system into a group of smaller, stable, and sustainable subsystems, also called islands. After a controlled system separation, power system operators should resynchronize and reconnect each island to restore the system. In this sense, real-time knowledge of the operating condition of the islands is required. In this paper, a real-time ICI and restoration scheme is proposed. The proposed scheme consists of an ICI algorithm that finds islanding solutions with minimal power-flow disruption while considering power system restoration constraints (e.g., blackstart availability, sufficient generation capacity and observability), a real-time state estimator that monitors the system before and after the islanding, and a restoration process. The proposed ICI and restoration scheme is tested using the dynamic IEEE 39- and 118-bus test systems.

Modern power systems face significant challenges in state estimation and real-time monitoring, particularly regarding response speed and accuracy under faulty conditions or cyber-attacks. This article proposes a hybrid approach using physics-informed neural networks (PINNs) to enhance the accuracy and robustness of power system state estimation. By embedding physical laws into the neural network architecture, PINNs improve estimation accuracy for transmission grid applications under both normal and faulty conditions, while also showing potential in addressing security concerns, such as data manipulation attacks. Experimental results show that the proposed approach outperforms traditional machine learning models, achieving up to $\sim$83% higher accuracy on unseen subsets of the training dataset and $\sim$65% better performance on entirely new, unrelated datasets. Experiments also show that during a data manipulation attack against a critical bus in a system, the PINN can be up to $\sim$93% more accurate than an equivalent neural network.