Hoang Anh Pham

Wind-power ramp smoothing with hybrid energy storage must reduce grid-side excursions without assuming unlimited storage headroom or hiding battery-supercapacitor cycling stress. This paper proposes a constraint-aware hierarchical HESS controller (CA-HESS) for grid-code-oriented wind-power smoothing under ramp-rate, power, energy, state-of-charge (SOC), and efficiency limits. The method first forms an SOC-aware grid-power reference and aggregate HESS request, tightening the smoothing target when headroom is available and relaxing it near SOC boundaries. The requested HESS power is then separated into slow battery support and fast supercapacitor support, after which one-step feasible projection enforces converter-power and SOC/energy limits and records the residual as unserved HESS demand. The controller is evaluated on a 20 MW wind plant with a 5 MWh battery and a 0.5 MWh supercapacitor using a 365-day, 1-min wind profile and a one-day, 1-s profile; an SOC-feedback HESS benchmark based on adaptive SOC-range control is reproduced under identical ratings, limits, efficiencies, and metrics. Annually, CA-HESS reduces the P99 ramp from 0.635 to 0.379 MW/min and the violation rate from 0.0341\% to 0.0122\%. Its battery cycling is 30.7 EFC, compared with 435.8 EFC for fixed LPF-HESS and 502.8 EFC for the SOC-feedback HESS benchmark. In the 1-s test, the maximum 60-s power change decreases from 2.651 to 0.742 MW. These results indicate that CA-HESS provides a balanced smoothing--cycling--feasibility compromise, while ramp compliance remains conditional on feasible HESS headroom.

This paper proposes a boundary-based minimum-injection high-frequency balancing strategy for low-speed standard half-bridge modular multilevel converter (MMC) motor drives to reduce the injected high-frequency balancing voltage and current and the associated electrical stress while maintaining the prescribed submodule capacitor-voltage ripple limit. The proposed method preserves the conventional Korn-type high-frequency injection (HFI) balancing path, but determines the commanded injection level from a constrained minimum-injection condition governed by the residual arm-energy demand and the available electrostatic buffering capability of the capacitor stack. An analytical operating boundary between HFI-assisted balancing and capacitor-based self-buffering is established by comparing the residual-energy requirement with the usable capacitor-voltage headroom constrained by modulation feasibility and the upper submodule voltage limit. Accordingly, the injection coefficient and the average capacitor-voltage reference are jointly coordinated so that the capacitor stack buffers the admissible residual energy, whereas the HFI channel supplies only the remaining balancing component required for ripple regulation. Comparative simulations against fixed HFI and a recent adaptive HFI benchmark demonstrate reductions of 33.0% in the mean compensation coefficient, 29.1% in the representative peak-to-peak submodule capacitor-voltage ripple, 13.7% in the representative arm-current RMS, 28.5% in the representative arm-current peak, and 25.3% in the current-squared loss proxy. The common-mode-voltage benefit is mainly reflected in the RMS and accumulated-burden indices rather than in the reduction of every instantaneous peak. These results verify that the proposed strategy reduces the injected high-frequency balancing content and the associated current- and voltage-side stresses under explicit ripple, modulation, and capacitor-headroom constraints.

This article presents a comprehensive scenario for utilizing drones to search for victims during high-rise fires. Given the complexity and difficulty of accessing such environments, employing drones for rapid victim searches may significantly reduce rescue operation time. Specifically, we have devised a two-phase drone control scenario. In phase 1, the drone autonomously navigates to the target location, benefiting from Deep Learning technology to automatically avoid obstacles during its movement. In the second phase, we have introduced a scanning algorithm to assist the drone in systematically surveying the entire rescue and recovery area to locate victims. We have constructed a test scenario and assessed the results using AirSim simulation software.