The parameters of power system electromagnetic transient (EMT) model have great influences on the accuracy of EMT simulation. This paper proposes a data-driven parameter calibration method based on Sobol sensitivity analysis and Gaussian mixture model (GMM) to calibrate the parameters of the power system EMT models. First, the dominant parameters of the power system EMT model are derived based on the derivative-free Sobol sensitivity analysis method. Then, the GMM that describes the relationship between the dominant parameters and the EMT simulation errors is established and solved. Finally, an improved particle swarm optimization algorithm is adopted to optimize the EMT simulation errors and the values of the parameters are obtained according to the minimum error and the conditional probability invariance of GMM. The test results on four different systems show that the proposed method can accurately calibrate all dominant parameters of the EMT models of the various power systems.

The rapid growth of renewable energy presents significant challenges for power grid operation, making the efficient integration of renewable energy crucial. This paper proposes a method to evaluate the power system’s capacity to accommodate renewable energy based on the Gaussian mixture model (GMM) from a symmetry perspective, underscoring the symmetrical interplay between load and renewable energy sources and highlighting the balance necessary for enhancing grid stability. First, a 10th-order GMM is identified as the optimal model for analyzing power system load and wind power data, balancing accuracy with computational efficiency. The Metropolis–Hastings (M-H) algorithm is used to generate sample spaces, which are integrated into power flow calculations to determine the maximum renewable energy integration capacity while ensuring system stability. Short-circuit ratio calculations and N-1 fault simulations validate system robustness under high renewable energy integration. The consistency between the results from the M-H algorithm, Gibbs sampling, and Monte Carlo simulation (MCS) confirms the approach’s accuracy.

Aiming at the problem of ultra-low-frequency oscillation (ULFO) caused by an irrational setting of governor parameters of high-head hydraulic turbine units, this paper proposes a method to optimize the additional damping control parameters of the governor to suppress ULFO. Firstly, the mechanism of ULFO is elaborated and the effect of system damping torque on ULFO is analyzed. Secondly, Hamilton’s theory is used to rank the magnitude of each unit’s contribution to the ULFO. Then, the parameters of the damping controller are optimized using the variational modal decomposition–Hilbert transform method and the Grey Wolf Optimization algorithm. It realizes the phase compensation of the hydroelectric unit governor and suppresses the ultra-low-frequency oscillation. Finally, the proposed method is validated using the simulation conducted in CloudPSS. The test results indicate that the proposed method can effectively suppress the ULFO caused by the high-head hydroelectric unit.

With the large-scale integration of wind power, it is essential to establish an electromagnetic transient (EMT) model of a wind turbine system. Focusing on the problem of the difficulty in obtaining the parameters of the direct-driven permanent magnet synchronous generator (PMSG) model, this manuscript proposes a method based on trajectory sensitivity analysis and improved gray wolf optimization (IGWO) for identifying the parameters of the PMSG EMT model. First, a model of a PMSG wind turbine is established on an EMT simulation platform. Then, the key parameters of the model are determined based on the sensitivity analysis. Five control parameters are selected as the key parameters for their higher sensitivity indexes. Finally, the key parameters are accurately identified, using the proposed IGWO algorithm. The final case study demonstrates that the proposed IGWO algorithm has better optimization performance compared with the GWO algorithm and particle swarm optimization (PSO) algorithm. In addition, the simulation waveforms show that the identified parameters are accurate and applicable to other operating conditions.