With the increasing integration of renewable generation, many power grids have gradually formed AC–DC hybrid systems. Abnormal operations, such as DC blocking faults and generation trips, have led to several incidents of large frequency deviations. However, current simulation methods result in large errors when estimating the frequency regulation capacity of the system. This paper proposes a generic system frequency-response (SFR) model that can be used to estimate the dynamic frequency behavior of modern large-scale power systems. The limitations of the classical SFR model is first analyzed. Second, a generic SFR model with a more reasonable structure is presented, and the parameter-determination strategy is proposed using both the dynamic and steady-state data. Then, the generic SFR model is built and verified by a simulation case. Finally, a generic SFR model with satisfactory accuracy is established for the power grid in East China based on the measured disturbance data. The results show that the proposed model is promising for broad potential applications.

The wind speeds of each wind turbine generator (WTG) are the basis for establishing an aggregated model of a wind farm for power system transient simulation. However, the commonly obtained operation data of the wind farm do not include the wind speeds of each WTG. Thus, a modeling method for the wind speeds of the WTGs of same moment inside the wind farm was proposed. A wind speed combination model (WSCM) is established via K-means clustering from the sorted field-measured wind speed data, which can provide reasonable wind speeds for each WTG when aggregate modeling the wind farm. Subsequently, an improved two-step clustering method of the WTGs was proposed, by which the WTGs are initially clustered according to whether they enter low voltage ride through (LVRT) mode or not. An “N-1 VS One” aggregated model and the binary search algorithm were used to quickly and correctly predict whether a WTG in the wind farm enters LVRT mode under a grid fault. The case study demonstrates the satisfactory performance of the WSCM and the necessity of clustering the WTGs according to whether they enter or do not enter LVRT mode for obtaining an accurate aggregated model of the wind farm. All the data, models, and the programs used in this paper can be found at DOI: 10.21227/dqyk-t852.

Recently, with the increased intermittent renewable energy penetration, many power grids have been incorporated into a large‐scale long‐distance UHV AC–DC hybrid system. In the actual grid, power disturbances, such as DC blocking faults and trip‐off of wind turbines, often occur, resulting in power shortages and large frequency fluctuations. However, the existing approaches to assess the system frequency security are not reliable. This paper proposes an analytic formula for the system frequency response based on a generic system frequency‐response (SFR) model, which can be applied to modern large‐scale power systems. First, a generic SFR model with a reasonable structure is designed according to the parameter determination strategy. Second, according to the transfer function in the model, the time‐domain analytic formula of the frequency response is obtained by realizing the inverse Laplace transform. Moreover, four main indexes are established to represent the characteristics of the frequency dynamic process in different periods, and these indexes are qualitatively and quantitatively analysed. Finally, the New England 10‐unit 39‐bus power system and East China Power Grid are considered to demonstrate the key features of the proposed method. The results show that the proposed analytic assessment method can be effectively adopted in several applications.

In the process of parameter identification, sensitivity analysis is mainly used to determine key parameters with high sensitivity in the model. Sensitivity analysis methods include local sensitivity analysis (LSA) and global sensitivity analysis (GSA). The LSA method has been widely used for power system parameter identification for a long time, while the GSA has started to be used in recent years. However, there is no clear conclusion on the impact of different sensitivity analysis methods on parameter identification results. Therefore, this paper compares and studies the roles that LSA and GSA can play in different parameter identification methods, providing clear guidance for the selection of sensitivity analysis methods and parameter identification methods. The conclusion is as follows. If the identification strategy that only identifies key parameters with high sensitivity is adopted, we recommend still using the existing LSA method. If using a groupwise alternating identification strategy (GAIS) for high- and low-sensitivity parameters, either LSA or GSA can be used. To improve the identification accuracy, it is more important to improve the identification strategy than to change the sensitivity analysis method. When the accuracy of the non-key parameters with low sensitivity cannot be confirmed, using the GAIS is an effective method for ensuring identification accuracy. In addition, it should be noted that the high sensitivity of a parameter does not necessarily mean that the parameter is identifiable, which is revealed by the examples used in this paper.