A multi-rate parallel real-time simulation method based on FPGA–CPU is studied to realize the asynchronous co-simulation of the converter of doubly fed wind power systems with the wind turbine and external power grid. The doubly fed wind power system is partitioned by simulation step length, and the partitioned small-step-length data are processed using integral homogenization. For large-step data, an improved delay-compensated linear interpolation method combined with Newton interpolation is proposed for processing. The general small time-step (GST) model method is used to implement the FPGA modeling of the small-step converter, and resource optimization is achieved through timing time-division multiplexing. Asynchronous parallel co-simulation of a doubly fed wind power system is implemented on an FPGA–CPU co-simulation platform. Among them, the FPGA realizes the development of the converter HDL with a small step of 1 $\mathsf{\mu }\mathrm{s}$, while the CPU completes the simulation of the wind turbine and power grid synchronously with a large step of 50 $\mathsf{\mu }\mathrm{s}$. Finally, by comparing with MATLAB/Simulink offline simulation and analyzing the error, it is concluded that the simulation accuracy of the improved method in this paper is higher than that of the un-interpolated parallel simulation, which verifies the real-time performance and accuracy of the modeling and improved method in this paper.

In recent years, the power system transient stability assessment (TSA) based on a data-driven method has been widely studied. However, the topology and modes of operation of power systems may change frequently due to the complex time-varying characteristics of power systems. which makes it difficult for prediction models trained on stationary distributed data to meet the requirements of online applications. When a new working situation scenario causes the prediction model accuracy not to meet the requirements, the model needs to be updated in real-time. With limited storage space, model capacity, and infinite new scenarios to be updated for learning, the model updates must be sustainable and scalable. Therefore, to address this problem, this paper introduces the continual learning Sliced Cramér Preservation (SCP) algorithm to perform update operations on the model. A deep residual shrinkage network (DRSN) is selected as a classifier to construct the TSA model of SCP-DRSN at the same time. With the SCP, the model can be extended and updated just by using the new scenarios data. The updated prediction model not only complements the prediction capability for new scenarios but also retains the prediction ability under old scenarios, which can avoid frequent updates of the model. The test results on a modified New England 10-machine 39-bus system and an IEEE 118-bus system show that the proposed method in this paper can effectively update and extend the prediction model under the condition of using only new scenarios data. The coverage of the updated model for new scenarios is improving.

As an important part of the DC micro-grid, DC solid-state transformers (DCSST) usually use a dual-loop control that combines the input equalization and output voltage loop. This strategy fails to ensure output equalization when the parameters of each dual active bridge (DAB) converter module are inconsistent, thus reducing the operational efficiency of the DCSST. To solve the above problems, a DCSST-balancing control strategy based on loop current suppression is presented. By fixing the phase-shifting angle within the bridge and adjusting the phase-shifting angle between bridges, the circulation current of each DAB converter module is reduced. Based on the double-loop control of the DAB, five controllers are nested outside each DAB submodule to achieve distributed control of the DCSST. The proposed control strategy can reduce the system circulation current with different circuit parameters of the submodules, ensure the balance of input voltage and output current of each submodule, and increase the robustness of the system. The simulation results verify the validity of the proposed method.