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Low-power line-frequency transformers are common across several applications, but these transformers are found to have large standby losses, low efficiencies, large weights, and high costs. In this paper we optimize a power conversion stage for this and other applications where an unregulated, isolated converter is needed. The selected topology uses low-impedance passives to reduce their size; to address this case we analyze operation of a lower-Q resonant tank. Lastly, because of the application of low-power line-frequency transformers, we optimize around a typical usage cycle to minimize the yearly average power loss. For the application of a Class 2 line-frequency transformer, model results are accurate to simulation by within 12.1%, and optimization produced a design that was experimentally validated to achieve a yearly average power loss of 285.1 mW, a peak efficiency of 99.26% and a standby loss of 155 mW. 

Class 2 transformers are small line-frequency transformers widely used for control systems requiring 24 VAC signaling, including residential and commercial HVAC systems, industrial controls, and much more. These transformers have large standby losses, low efficiencies, large weights, and high costs. In this work, we propose a power electronic alternative in the form of a low-power solid-state transformer. We design and test two 40 VA 120 VAC to 24 VAC solid state transformers, including a two-stage and a single-stage topology. Both converters provide higher efficiencies across all load ranges compared to the Class 2 line-frequency transformers, especially at light load (5 VA) with improvements of 19.6% and 29.9%, respectively. Standby losses for the two are 417 mW and 196 mW, compared to an average of 2.8 W standby loss for 40 VA Class 2 line-frequency transformers. 

Class 2 transformers are small line-frequency transformers that are widely used for control systems that require 24 VAC signaling, including residential and commercial HVAC systems, industrial control systems, doorbells, and much more. In this work, we sampled and tested seven Class 2 transformers, each across different operating conditions, in order to characterize their efficiencies and note their shortcomings. We also provide possible improvements and solutions. We see on average a peak efficiency of 84.43% with 5.37 W of power loss when operated at 75% (30 VA output power) of their rated power, a 1.84% efficiency drop from the temperature rise that occurs at steady state when operated with full load, 2.8 W of no-load loss at 120 VAC input, and a no-load loss contribution of over 50% when operating at less than 75% load power. With these values, there is a clear goal to strive for in order to improve or create an alternative to these Class 2 transformers. 

Seven different miniaturized low-power isolation transformer topologies are analyzed using FEA simulation. Performance optimizations of fixed-area transformers with different operation frequencies and heights, with and without magnetic cores, are performed. The comparison of optimization results reveals performance potential and trade-offs of the different topologies under different restrictions. More than 1 W of power transfer is possible at more than 85% efficiency in 1 mm2 of footprint area for several different topologies. 

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.