Miao Yu

Renewable generation and load uncertainty pose significant challenges to power system security, necessitating efficient approaches to characterizing high-dimensional security regions. To overcome the curse of dimensionality, uncertainty neglect, and undue conservatism in existing methods, this paper proposes an approach integrating distributionally robust optimization (DRO) and deep learning for security region characterization. First, to properly account for uncertainty while avoiding excessive conservatism, a DRO-based active search strategy is developed to identify critical boundary points, where diffusion-generated renewable scenarios and load-deviation samples constructed around typical demand profiles are jointly used to build a robust probabilistic ambiguity set. Subsequently, a Transformer-based model learns from these boundary points to reconstruct the full high-dimensional security region. The model's self-attention mechanism captures the global nonlinear dependencies among dimensions, enabling a precise and efficient boundary fit. Simulations on IEEE test systems confirm that the approach accurately characterizes high-dimensional security regions at a low computational cost, yielding a security region with strong robustness to renewable-load uncertainty. This work offers a new paradigm for security assessment and decision support in power systems under high uncertainty.

Distributed consensus control, with its advantages of higher efficiency and better accuracy, has gradually replaced centralized control and become a common method to regulate voltage and share current in DC microgrids. However, the false data injection attacks targeting communication channels bring great security risks to DC microgrids, and existing resilient control methods are still plagued by their poor applicability and high implementation costs. To this end, this article proposes an event-triggered communication-based consensus control scheme, which consists of a decentralized fault-tolerant controller using input–output feedback linearization and a distributed attack observer based on the dual-layer cyber network with the event-triggered communication strategy. Lyapunov analysis method proves that the proposed scheme can effectively resist the unbounded false data injection attacks and guarantee the resilient control of bus voltage regulation and current sharing for DC microgrids. Finally, a DC microgrid model is built in the hardware-in-the-loop testbed to validate the effectiveness and superiority of the proposed scheme.

Ultra-high-field (7 T) MRI offers high SNR but suffers from severe B₁⁺ inhomogeneity and standing-wave effects. Although traveling-wave (TW) propagation in an ideal homogeneous medium maintains constant field amplitude and therefore suggests intrinsic advantages for UHF excitation, practical MRI environments introduce conductive and dielectric loading, radiation losses, and mode mismatches that degrade TW efficiency and uniformity. Conventional dielectric-based approaches rely on a single dielectric structure, which cannot simultaneously optimize transmit efficiency, field homogeneity, and wave-impedance matching. To address this limitation, this study introduces a double-structure dielectric design that integrates a dielectric waveguide with a separate dielectric pad to maximize power-transmission efficiency while also improving field uniformity. The combined structure was optimized with respect to geometric parameters and placement. Simulations show that, compared with one structure, double-structure predict a 33.3% increase in P_tissue. Experimental validation confirms a 39.4% gain in excitation efficiency over the conventional TW MRI through using the proposed double-structure dielectrics.

Triangular current mode (TCM) control is popularly applied in low-to-medium power grid-tied inverters (GTIs) due to its excellent ability of realizing full-range zero-voltage-switching. However, practical circuit nonidealities, including time delay and the resonance occurring between the power inductor and parasitic output capacitances of the power switches, make actual inductor current boundary diverge from the required one, which degrades the control accuracy and thereby deteriorates the grid current total harmonic distortion. To tackle this issue, an improved inductor current compensation scheme for TCM-controlled GTIs is proposed. Taking the single-phase full-bridge GTI as an example, the adverse effects of time delay and resonance process on TCM control are separately modeled and analyzed. Then, an accurate operation model of TCM-controlled GTIs considering these two circuit nonidealities is developed. In this model, a charge-based resonance behaviors estimator is proposed for estimating the pivotal current and time parameters of the resonance process with simple forms. Further, elaborations of the proposed scheme, parameters design and control implementation are conducted. Finally, the efficacy and advantages of the proposed scheme are verified through the simulations and experiments on a 300-W GTI prototype.