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).@en

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.@en

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.@en

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.@en