Publications

With the growing capacity of renewable energy, grid-forming (GFM) inverters are considered as promising solutions for low-inertia power grids. However, the overcurrent problem of GFM inverters during large grid disturbances is still a significant challenge. Generally, the GFM inverter has two types of overcurrent problems: 1) the steady-state overcurrent; and 2) the transient overcurrent. To address the steady-state overcurrent problem, a virtual power angle limiter method has been proposed in existing literature, which can limit steady-state overcurrent and maintain transient stability simultaneously during grid faults. Nevertheless, the transient overcurrent problem has not been fully addressed, which is still a remaining issue. To address the transient overcurrent problem, a hybrid transient virtual resistor (HTVR) method is proposed in this article, which can increase the generating speed of the transient virtual resistor as fast as possible. Thus, the transient overcurrent of the GFM inverter can be restrained below 1.2 or 1.1 p.u. in the grid fault and recovery scenarios by using the HTVR method. Simulation and experiments verify the effectiveness of the proposed method.

To evaluate the performance of dual-active-bridge (DAB) converters across various topologies and control schemes for optimal design, this article proposes a generic explicit digital model applicable two-level (2L)/three-level (3L) half-bridge (HB), full-bridge (FB), and three-phase (3P) topologies with a unified control structure. Voltage and current expressions for a single bridge leg considering both 2L and 3L topologies are developed in a generalized manner, and thus the model of DAB topologies with different number of legs can be constructed by superimposing the corresponding number of individual bridge leg models. Subsequently, the digital model, i.e., discrete-time model of DAB converters with different topologies and control configurations, is derived based on the circuit differential equations and a unified closed-loop control system by applying backward Euler method. Therefore, both steady-state and dynamic performance of DAB converters can be predicted in the proposed model. Owing to the analytical formulation of operating parameters and generic expressions for different topologies and control, the calculation speed of the proposed digital model can be accelerated compared to commercial simulation platforms such as MATLAB/Simulink and PLECS, and the design efficiency can be significantly enhanced, especially when it eliminates the need for frequent manual adjustments of topologies, modulation, and loss-thermal models of power devices. Simulation and experimental tests validate that the digital DAB model can achieve high accuracy under various topologies with fast execution speed.

Hybrid AC/DC microgrids with distributed energy storage (DS) improve power reliability in remote areas. Existing power management methods either focus on steady-state power sharing or transient inertia support, but rarely combine both. They also often ignore frequency and voltage deviations caused by droop control, which can harm sensitive loads. To overcome these issues, this paper proposes a full-time-scale (FTS) power management strategy that unifies transient inertia sharing and steady-state power allocation through a novel dynamic concatenator. It also introduces autonomous frequency/voltage restoration to eliminate steady-state deviations in each subgrid. Additionally, a global equivalent circuit model (GECM) is developed to simplify system analysis and design. Experiments confirm that the approach maintains nominal frequency and voltage in steady state while enabling seamless transition between transient inertia support and proportional power sharing across all time scales.

System-level condition monitoring methods estimate the electrical parameters of multiple components in a converter to assess their health status. The estimation accuracy and variation can differ significantly across parameters. For instance, inductance estimations are generally more accurate and stable than inductor resistance in a buck converter. However, these performance differences remain to be analyzed with a more systematic approach otherwise the condition monitoring results can be unreliable. Therefore, this paper analyzes the training loss landscape against multiple parameters of a buck converter to provide a systematic explanation of different performances. If the training loss is high and smooth, the estimated circuit parameter typically is accurate and has low variation. Furthermore, a novel physics-informed neural network (PINN) is proposed, offering faster convergence and lower computation requirements compared to an existing PINN method. The proposed method is validated through simulations, where the loss landscape identifies the unreliable parameter estimations, and the PINN can estimate the remaining parameters.

Grid-forming (GFM) converters have emerged as essential technologies for ensuring the stability of power systems dominated by renewable energy sources. Nevertheless, their restricted overcurrent capacity may trigger protection during grid faults. While existing studies emphasize the design and optimization of overcurrent mitigation strategies, precise characterization of fault current and dynamic analysis of transient inrush current are still lacking, leading to occurrences of protection malfunction. To bridge this research gap, this article considers the impact of filter parameters and current loop parameters on the equivalent impedance of the converter, and on this basis, establishes a precise fault current characterization model for GFM converters. Based on the model, the key factors influencing inrush current can be analyzed, including voltage amplitude, impedance magnitude, and power angle. Finally, a precise virtual impedance-based inrush current suppression method is proposed, which effectively limits the inrush current to the desired level while ensuring system stability. Both theoretical analysis and experimental results are carried out to validate the robustness and adaptability of the proposed strategy under various grid strengths and grid voltage sags.