Efficient Multiphysics Circuit Simulation for Transformer Optimization Using Hybrid Dowell Artificial Neural Network

カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門（英文）

発行日: 2024/05/01

タイトル(英語): Efficient Multiphysics Circuit Simulation for Transformer Optimization Using Hybrid Dowell Artificial Neural Network

著者名: Yu-Hsin Wu (Electrical Engineering, Nagoya University), Koichi Shigematsu (Institute of Materials and Systems for Sustainability, Nagoya University), Yasumichi Omoto (Power Electronics Division, NIDEC MOBILITY CORPORATION), Yoshihiro Ikushima (Power Elect

著者名(英語): Yu-Hsin Wu (Electrical Engineering, Nagoya University), Koichi Shigematsu (Institute of Materials and Systems for Sustainability, Nagoya University), Yasumichi Omoto (Power Electronics Division, NIDEC MOBILITY CORPORATION), Yoshihiro Ikushima (Power Electronics Division, NIDEC MOBILITY CORPORATION), Jun Imaoka (Institute of Materials and Systems for Sustainability, Nagoya University), Masayoshi Yamamoto (Institute of Materials and Systems for Sustainability, Nagoya University)

キーワード: transformer，leakage inductance，modeling，neural network，machine learning，optimization

要約(英語): This paper introduces a practical approach for transformer design optimization using a novel hybrid Dowell artificial neural network (HDANN) model. This model is a highly efficient and accurate method to estimate leakage inductance, which is an important parameter that affects the performance of transformers and power converters. The model combines the conventional hybrid Dowell's model, which uses analytical equations, and an artificial neural network, which uses machine learning techniques. It is integrated into an optimization program to optimize the design of a transformer in terms of its size and loss. We investigated the HDANN design scope by testing various transformer conditions. The results provided an in depth understanding of the capabilities and limitations of the HDANN model for transformer design. By understanding the HDANN design scope, the optimization program was implemented in a multidomain circuit simulation, which includes electric and magnetic circuits. This allows a high-speed co-simulation using the optimized transformer design that considers the geometry and material characteristics for the desired circuit specification. It showed that the HDANN offers significant advantages over existing design optimization methods, including improved ease of application, accuracy, and efficiency. The effectiveness of the optimization using the HDANN with the defined geometric parameters was demonstrated by the circuit analysis results of a phase-shift full-bridge converter. Summarizing, the proposed method can potentially revolutionize how transformers are designed and implemented for various applications, leading to increased design reliability as well as reduced power loss and size.

本誌: IEEJ Journal of Industry Applications Vol.13 No.3 （2024）

本誌掲載ページ: 327-337 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejjia/13/3/13_23009691/_article/-char/ja/