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 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.

This paper presents the design and optimization of a Medium Frequency Transformer (MFT) for use in Solid State Transformer (SST) systems supporting green hydrogen production. Operating at 1 kHz and integrated within an LLC resonant converter, the transformer is optimized for minimal weight and high efficiency while ensuring adequate leakage inductance and insulation performance. A core-type configuration with cylindrical windings was selected based on FEM simulations and mass-efficiency trade-offs. The final prototype, using copper conductors, achieves 97.8% efficiency with a mass below 50 kg and meets the required 7 mH leakage inductance. High-voltage testing, including partial discharge and breakdown tests, confirmed the insulation coordination of the design. The results demonstrate a practical and scalable approach for high-performance SST integration in renewable energy applications.

This paper presents the prototype development and performance evaluation of a three-stage flyback-based Input Series Converter designed as an Auxiliary Power Supply (APS) for a Modular Multilevel Converter (MMC)-based Arbitrary Waveform Generator (AWG). The APS is connected across the submodule capacitors of the MMC and converts the capacitor voltage to 24V, providing power to the gate drivers and control units within each submodule. The proposed converter features integrated active Input Voltage Sharing (IVS), a scalable architecture, and wide-input voltage operation. A multi-winding flyback transformer ensures high-voltage insulation between the submodule capacitor and the APS output while facilitating active IVS for balancing input capacitor voltages. A transformer prototype has been developed and tested in an open-loop converter configuration. The system’s performance has been evaluated at an output power of 30W across an input voltage range of 300V–3600V.

This paper discusses the prototype development and testing of a load invariant, high voltage gain DC power supply using inductive power transfer. The developed supply is intended to power the submodules of the CHB based arbitrary waveshape generator for testing high voltage components and materials. Using the Series Parallel topology, a load invariant voltage gain of 15 is obtained from a 350 V input DC Bus, with an efficiency of 88% and a load regulation of 18%. The obtained 5 kV output can conduct DC breakdown of dielectrics with a damping resistor and can test insulation aging, breakdown under high frequency AC.

To enhance the voltage-handling capability of a switch, the series connection of switching devices is a cost-effective method that preserves many advantages of mature low-voltage devices. Dynamic voltage imbalance and electrical isolation for the devices at the high voltage (HV) side are two important challenges associated with series connection topology. Transformer-coupled gate drivers are excellent for providing both dynamic voltage balance and high galvanic isolation. However, they can only provide the switching function at the transformer pulse frequency. To generate complex waveforms of future power-electronics-dominated grids, a switch with user-defined turn-on/off timing is required for testing grid assets under high-voltage conditions. This article presents a simple, cost-effective open-loop gate driver that overcomes this limitation by introducing two sets of complementary pulse transformers to initialize programmable frequency and duty cycle. Successful experimental verification of the series-connected SiC mosfets prototype is performed at 3.2 kV at various frequencies and duty cycles. The article also demonstrates that the measurement probes placed across series-connected mosfets significantly affect the voltage distribution and validate a compensation mechanism.

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.

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.

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.

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.

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).

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.