by Eunchong Noh 

 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science (Electrical and Computer Engineering) December 2021 

Thesis committee: Gyu-Sik Kim, Professor, ECE, University of Seoul Seung-Hwan Lee, Associate Professor, ECE. University of Seoul Moon-Que Lee, Professor, ECE, University of Seoul

The demand for isolated DC-DC converters is increasing due to the spread of electric vehicles (EV) and the expansion of renewable energy use. Accordingly, the need for a high-frequency Transformer, a key component of an isolated DC-DC converter, is also increasing. This trend is also taking place in the field of railway locomotive systems. Solid state transformer (SST) technology to improve the performance and efficiency of railway locomotive propulsion systems is being actively researched, and high-frequency transformer is the core of SST. Highfrequency transformer design for railway locomotive systems has more complex design elements that must be considered for volume-loss optimization and insulation and thermal design. This thesis investigates an optimization design methodology using machine learning and NSGA-II for optimized high-frequency transformer design. For machine learning, Finite-element analysis (FEA) simulation was used to obtain high-frequency transformer parameter data. Conventional high-frequency transformer optimization design methods used analytical models for parameter calculation. However, this analytical model has a significant error when the shape of the high-frequency transformer becomes complicated. In particular, the leakage inductance of the high-frequency transformer is difficult to calculate with an analytical model. So, it is difficult and cumbersome to apply it in the design. This thesis obtained magnetizing inductance, leakage inductance, and copper loss of shell-type transformer models in various shapes using FEA simulation. Then, using the data obtained from the FEA simulation, a machine learning regression model was created to minimize the parameter calculation error in complex shapes. In addition, the NSGA-II algorithm, which is widely used in multi-variable optimization design, is used to find the optimal transformer shape to perform optimization that can satisfy multiple design elements at the same time. Each parameter inferred by the machine learning regression model showed a high correlation and sufficiently low inference error rate, used for parameter calculation in the NSGA-II algorithm. The inferred parameters are used to calculate transformer loss for optimization design or check whether constraints are satisfied. Through the optimization design using NSGA-II, a Pareto front curve for volume and loss that satisfies all design conditions was obtained. The designer can select and use the designs according to the situation. The methodology can be designed for more complex shapes to achieve higherperformance high-frequency transformer design. In addition, the complexity of the design is reduced because numerous consideration factors can be easily considered through constraint setting in the NSGA-II. Finally, unlike the conventional design methodology, which has a significant influence on the skill and intuition of the designer, once the environment is set up, the design proceeds only by inputting target parameters and executing the code so that the design time can be reduced. Therefore, it is possible to design a high-frequency transformer with constantly high performance regardless of the designer's skill level.