This study presents a current balancing technique for high-current windings in medium-frequency transformers (MFTs), particularly relevant to solid-state transformer (SST) applications. Handling high currents on the low-voltage high-current winding of MFTs is challenging due to skin and proximity effects. Conventional techniques, such as continuously transposed conductors (CTCs) and parallel winding paths, are applicable but have limitations in medium-and high-frequency applications such as SSTs due to skin and proximity effects. To address these issues, a modular and tunable compensation method is proposed, based on adding small, series-connected inductive elements (compensation toroids) to each parallel winding path. Experimental results from a prototype validate the proposed compensation technique, highlighting its effectiveness in mitigating unbalanced current distribution. Finite element analysis (FEA) and experimental validation across a wide frequency range (1–10 kHz) confirm the effectiveness of the method. The results demonstrate a significant reduction in current imbalance with minimal added losses or system impact.

The increasing urgency of global decarbonization has accelerated the transition renewable-based energy systems. Among various technological pathways, green hydrogen has emerged as a critical energy vector due to its potential for large-scale storage and carbon-neutral applications. However, the current production of hydrogen remains largely dependent on fossil fuels, which undermines its environmental benefits and long-term sustainability. To fully realize the promise of green hydrogen, scalable and efficient power conversion systems are needed to interface renewable energy sources with electrolyzer systems.
In particular, large-scale onshore hydrogen production requires power conversion systems that are compact, modular, and capable of operating at medium to high voltage levels. Solid-state transformers (SSTs), which employ medium-/high-frequency galvanic isolation and integrated power electronic converters, provide a promising solution to meet these requirements. Compared to conventional line-frequency transformers, SSTs enable reduced volume and weight and enhanced controllability. This thesis investigates the optimal design, control strategy development, and loss modeling of solid-state transformers tailored for hydrogen electrolyzer systems, addressing key challenges in topology selection, loss estimation, and system performance optimization....

This paper presents a practical approach to reduce the size of medium-frequency, medium-voltage dry-type transformers through the innovative use of semiconductive screening. The proposed method minimizes the required air gaps, a critical aspect of dry-type transformer design, particularly for medium-frequency applications. Analytical approaches and Finite Element Method (FEM) simulations in COMSOL are used to demonstrate how to achieve a uniform electric field distribution within the transformers. Experimental investigations by means of partial discharge measurement on a prototype epoxy-based stress cone termination with a semiconductive shield are conducted. The results demonstrate the potential for this method to enhance transformer performance and provide a foundation for further advancements in medium-frequency transformer design.

This paper introduces a novel control strategy for Modular Multilevel Resonant converters (MMR) in Solid-State Transformer (SST) applications, with a focus on medium-voltage conversion for hydrogen electrolyzers. The article first reviews voltage control methods in MMR, analyzing their operational principles and regulation capabilities. A continuous modulation index control method with double-step staircase waveform modulation is then proposed, simplifying the control scheme to a single control variable while maintaining robust controllability. Meanwhile, the proposed approach maintains comparable power loss and harmonic performance to existing methods under the investigated operating conditions. Simulations and experiments are conducted to verify the feasibility and practical implementation of the proposed approach.

This article 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 state-of-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.

Traditional methods such as Steinmetz's equation (SE) and its improved variant (iGSE) have demonstrated limited precision in estimating power loss for magnetic materials. The introduction of Neural Network technology for assessing magnetic component power loss has significantly enhanced accuracy. Yet, an efficient method to incorporate detailed flux density information—which critically impacts accuracy—remains elusive. Our study introduces an innovative approach that merges Fast Fourier Transform (FFT) with a Feedforward Neural Network (FNN), aiming to overcome this challenge. To optimize the model further and strike a refined balance between complexity and accuracy, Multi-Objective Optimization (MOO) is employed to identify the ideal combination of hyperparameters, such as layer count, neuron number, activation functions, optimizers, and batch size. This optimized Neural Network outperforms traditionally intuitive models in both accuracy and size. Leveraging the optimized base model for known materials, transfer learning is applied to new materials with limited data, effectively addressing data scarcity. The proposed approach substantially enhances model training efficiency, achieves remarkable accuracy, and sets an example for Artificial Intelligence applications in loss and electrical characteristic predictions with challenges of model size, accuracy goals, and limited data.

The State of Health (SOH) is a crucial component of battery management systems (BMSs), offering important health information and protection against unsafe usage. In this paper, an accurate model for SOH estimation of Li-ion batteries was developed, which is uniquely characterized by using only the imaginary part of impedance at a specific frequency for precise SOH estimation. Through the identification of the relationship between impedance at a specific frequency and capacity degradation using correlation coefficients, the feature data most closely related to battery aging was selected. Next, the battery aging modeling and SOH estimation were validated on nine batteries across three different temperatures using a Feed-forward Neural Network (FNN). The validation results indicated that the proposed method has a high estimation accuracy, achieving a Mean Absolute Percentage Error (MAPE) of merely 2.05% throughout the entire lifecycle of the battery 45C02 during tests at a temperature of 45°C.

In the production of green hydrogen, electrolyzers draw power from renewable energy sources. In this paper, the design of Solid State Transformer (SST) for large-scale H ₂ electrolyzers is benchmarked. The three most promising topologies are chosen for design and comparison, including Modular Multi-level Converter (MMC) based SST, Modular Multi-level Resonant (MMR) based SST, and Input-Series-Output-Parallel (ISOP) based SST. The distance between converter towers for insulation and maintenance, the insulation system of the transformer, and the cooling system are designed with practical considerations in order to have an accurate estimation of the volume and weight of the SST. Losses in the switches are calculated based on equations, and losses in passive components are calculated based on FEM simulation. The operating frequency for each topology is optimized to minimize loss, weight, and volume. The best of each topology is then compared with each other to identify the most suitable one for large-scale H ₂ electrolyzers.

Resonant converters are popular in power electronics due to their soft-switching capabilities, which enhance efficiency and prolong component lifetime. Three- phase resonant converters are particularly noteworthy for their higher power density and reduced ripple, making them ideal for demanding applications. A critical aspect of optimizing three-phase LLC resonant converters is the design of a transformer with adequate leakage inductance required for the resonance circuit. This paper compares two distinct transformer designs for such converters: a five-limb shell-type transformer and a symmetrical triangular transformer. Both designs are evaluated in terms of their performance, efficiency, and suitability for integration into the converter architecture. A detailed design procedure using Finite Element Method (FEM) analysis is presented to guide the development of these transformers. The practicality of this approach and its effectiveness are demonstrated through the implementation of a 3.4 kV to 60 V, 50 kVA prototype. This work provides a comparative analysis of transformer designs and introduces a validated methodology for improving the performance of three-phase LLC resonant converters through optimized transformer design.

For electrolyzer applications, traditional solutions using line frequency transformers plus rectifiers are bulky, heavy and have low controllability. The Solid State Transformer (SST) could be a promising solution to solve the mentioned issues. This paper compares the semiconductor ratings and capacitance of five different Modular Multilevel Converter (MMC) based Solid State Transformer (SST) topologies. The results show that the DRU (Diode Rectifier Unit)-MMC based topologies have the lowest semiconductor ratings and capacitance. Because of the unidirectional power flow requirement, the source side MMC of Back-to-Back (BtB) MMC based SST could be replaced by DRU, thus the cost is drastically saved. Another interesting finding is that the DRU-MMC energy ripple is much lower than half of the energy ripple in BtB MMC, which is different from HVDC MMC.

Green hydrogen production uses renewable energies to energise the electrolysers for hydrogen production. The present paper compares possible solutions and configurations of a medium-frequency transformer (MFT) as part of a solid-state transformer (SST) in green hydrogen production applications. The single-phase and three-phase MFTs are compared and it is shown that a Yd three-phase MFT is the optimum choice for applications that require high power delivery and step-down of the voltage. A summary of previous works about MFT is also provided. Three-phase SST based on modular multilevel converters (MMC) is then described and various cases are investigated to obtain the optimum operational frequency. A 25 MVA, 400 Hz, 25.4 kV / 560V oil-immersed MFT design is presented and is shown that the proposed 400 Hz transformer saves 69% of the active parts' weight compared to a conventional line-frequency transformer (LFT).