Publications

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.

Generalized predictive control (GPC) stands out as a prominent representative in the predictive control family due to its robustness. As a crucial parameter in the GPC controller, the prediction horizon significantly impacts the control performance. Short prediction horizons may result in instability, whereas long prediction horizons can lead to a high computational burden but better stability. Although the conventional design approaches can be realized by empirical prediction horizon selection or incorporating nonlinear observers, a theoretical approach has been overlooked so far. To bridge this gap, this article introduces a novel approach to design prediction horizons for GPC demonstrated on a dc/dc boost converter. This method involves constructing a closed-loop system model and assessing the impact of different prediction horizons on the system. Based on the system modeling, a rigorous boundary for the prediction horizon is obtained to ensure control stability. Finally, the accuracy of the design method has been confirmed in experimental conditions. Notably, beyond establishing a rigorous prediction horizon boundary, the proposed method demonstrates a reduction in computational time by at least 22% compared with the empirically selected prediction horizons within the studied system.

Maintaining synchronization stability after large grid disturbances is a vital requirement for grid-forming (GFM) converters. Existing enhanced GFM control strategies are often designed for a specific disturbance scenario, requiring extended recovery times or exhibiting frequency fluctuations. This paper reveals that synchronization instability triggered by two typical grid disturbance events, phase jumps and voltage sags, can be unified according to underlying similarities during the post-disturbance recovery. Based on these insights, this paper proposes a novel voltage vector-informed grid-forming (VVI-GFM) control strategy by fully leveraging the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d$</tex-math></inline-formula>/<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> components of the point of common coupling (PCC) voltage vector, which can offer fast synchronization recovery and minimize frequency deviations under both phase jumps and voltage sags. The post-disturbance dynamics of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d$</tex-math></inline-formula>/<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> components of the PCC voltage vector are quantitatively characterized, thus mechanistically demonstrating the effectiveness and applicability of the VVI-GFM control. Moreover, detailed guidelines about the feedforward path design and critical control parameter calculation are provided to ensure normal operation across varying grid strengths. Simulations and experimental results under diverse grid disturbance scenarios further validate the advantages and robustness of the proposed control strategy.

To fully explore the frequency support ability of the cascaded multilevel converters (CMCs), this article has proposed an adaptive virtual inertia emulation and control scheme. First, the basic inertia emulation mechanism is introduced, which is achieved by absorbing and releasing the capacitor energy in the CMC. Second, considering the influence of reactive power generation of the CMC, the available variation range of the dc components of submodule (SM) capacitor voltages is redefined and recalculated. As a result, the maximum power exchange in SM capacitors in the CMC, which is used for frequency support, can be precisely characterized. Afterwards, the highest/lowest frequency value is estimated under different source/load power disturbance, and the adaptive virtual inertia emulation and control method is designed according to the dynamic frequency extremum estimation and the maximum variation range of SM capacitor voltages. Finally, simulation and experimental results verify that by the proposed method, the CMC can enhance its inertia support ability and reduce frequency deviation of the system. Under 0.02 p.u. load step condition, the frequency deviation is reduced by about 32.8%.

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.

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.