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Data-driven approach is promising for predicting impedance profile of grid-connected voltage source converters (VSCs) under a wide range of operating points (OPs). However, the conventional approaches rely on a one-to-one mapping between operating points and impedance profiles, which, as pointed out in this article, can be invalid for multiconverter systems. To tackle this challenge, this article proposes a stacked-autoencoder-based machine learning framework for the impedance profile predication of grid-connected VSCs, together with its detailed design guidelines. The proposed method uses features, instead of OPs, to characterize impedance profiles, and hence, it is scalable for multiconverter systems. Another benefit of the proposed method is the capability of predicting VSC impedance profiles at unstable OPs of the grid-VSC system. Such prediction can be realized solely based on data collected during stable operation, showcasing its potential for rapid online state estimation. Experiments on both single-VSC and multi-VSC systems validate the effectiveness of the proposed method. 

This paper investigates dynamic balancing of flying capacitor multilevel (FCML) converters with coupled inductors. Coupled inductors help to reduce the ripple current, accelerate transient response, and balance the flying capacitors of FCML converters at steady-state. However, coupled inductors also change the dynamic balancing properties compared to uncoupled inductors, and these principles must be understood for robust design. As an extension of a previously developed feedback mechanism for understanding the steady-state behaviors of coupled inductors in FCML converters, this paper derives models of coupled inductor FCML converters in dynamic operation, revealing several key insights: (i) the multi-resonant behavior of large-order FCML converters and their dependence on the initial conditions, (ii) how power dissipation relates to balancing speed, and (iii) the relation between multiphase and multilevel FCML balancing. The insights uncovered by this paper can provide useful guidelines for designing multi-phase self-balanced FCML converters with coupled inductors. 

This article investigates the modeling, analysis, and design methods for passively balancing flying capacitor multilevel (FCML) converters using coupled inductors. Coupled inductors synergize with FCML converters by reducing inductor current ripple, reducing switch stress, and, as proven in this article, by providing flying capacitor voltage balancing. This enables FCML topologies to be scaled well to larger systems. This article proves that coupled inductors can solve the unbalancing problem in many FCML converters. Moreover, tools are developed to thoroughly explain and quantify coupled inductor balancing, allowing general design guidelines to be offered for robust coupled inductor FCML converters. Finally, this article derives the limitations of coupled inductor balancing with respect to the number of phases, levels, and the required coupling ratio. The key principles of coupled inductor FCML balancing in steady state are demonstrated with a systematic theoretical framework and extensive experimental and simulation results. 

This paper summarizes the main results and contributions of the MagNet Challenge 2023, an open-source research initiative for data-driven modeling of power magnetic materials. The MagNet Challenge has (1) advanced the stateof-the-art in power magnetics modeling; (2) set up examples for fostering an open-source and transparent research community; (3) developed useful guidelines and practical rules for conducting data-driven research in power electronics; and (4) provided a fair performance benchmark leading to insights on the most promising future research directions. The competition yielded a collection of publicly disclosed software algorithms and tools designed to capture the distinct loss characteristics of power magnetic materials, which are mostly open-sourced. We have attempted to bridge power electronics domain knowledge with state-of-the-art advancements in artificial intelligence, machine learning, pattern recognition, and signal processing. The MagNet Challenge has greatly improved the accuracy and reduced the size of data-driven power magnetic material models. The models and tools created for various materials were meticulously documented and shared within the broader power electronics community. 

This paper introduces a capacitive differential wireless power transfer (DWPT) architecture to efficiently charge an array of unmanned aerial vehicles (UAVs) on a telecom tower as a UAV airport. A switched capacitor (SC) based ladder differential power processing (DPP) converter is utilized to regulate the voltages of multiple series-stacked wireless charging modules from a high-voltage DC bus. The half-bridge switches in the DPP circuit are reused as an inverter in a capacitive power transfer (CPT) system with a double-sided LC-compensation network, featuring reduced semiconductor component count and device stress. The capacitive coupling plates are integrated into landing platforms and UAV landing gears for high coupling capacitance and minimum influence on aerodynamics. An experimental prototype and related design considerations are presented to achieve high efficiency and ensure robust performance against misalignments. The DWPT architecture is verified through an 8-port DPP converter supporting up to 8 CPT charging modules.