TY - JOUR
T1 - High-Frequency Core Loss Modeling Based on Knowledge-Aware Artificial Neural Network
AU - Deng, Junyun
AU - Wang, Wenbo
AU - Ning, Zhansheng
AU - Venugopal, Prasanth
AU - Popovic, Jelena
AU - Rietveld, Gert
N1 - Publisher Copyright:
© 1986-2012 IEEE.
PY - 2024/2/1
Y1 - 2024/2/1
N2 - High-frequency core loss modeling plays a critical role in the magnetics design of power electronics. However, existing modeling tools fail to achieve both high speed and high precision. The conventional analytical approach enables fast estimations but performs poorly in accuracy. Magnetic loss models aided by loss maps feature high accuracy, but their model parameterization relies on large data. The emerging approach of artificial neural networks (ANNs) provides a promising alternative since it can achieve high speed and accuracy. However, conventional implementations of ANN require a large and accurate dataset for training, which is hard to achieve in magnetic loss modeling. To solve this problem, a knowledge-aware artificial neural network (KANN) is proposed that can achieve high accuracy with small training datasets. After introducing the principle of the proposed KANN, it is applied to high-frequency core loss modeling using the improved generalized Steinmetz equation as additional knowledge. To validate the performance of the proposed KANN-based design method for core loss modeling, it is applied to predict the losses of two ferrite cores in the frequency range of 50-450 kHz. The results show that the proposed method greatly outperforms present loss modeling approaches in accuracy and speed, requiring only a limited training dataset. An automatic loss modeling tool based on the new method is provided together with its open-source code.
AB - High-frequency core loss modeling plays a critical role in the magnetics design of power electronics. However, existing modeling tools fail to achieve both high speed and high precision. The conventional analytical approach enables fast estimations but performs poorly in accuracy. Magnetic loss models aided by loss maps feature high accuracy, but their model parameterization relies on large data. The emerging approach of artificial neural networks (ANNs) provides a promising alternative since it can achieve high speed and accuracy. However, conventional implementations of ANN require a large and accurate dataset for training, which is hard to achieve in magnetic loss modeling. To solve this problem, a knowledge-aware artificial neural network (KANN) is proposed that can achieve high accuracy with small training datasets. After introducing the principle of the proposed KANN, it is applied to high-frequency core loss modeling using the improved generalized Steinmetz equation as additional knowledge. To validate the performance of the proposed KANN-based design method for core loss modeling, it is applied to predict the losses of two ferrite cores in the frequency range of 50-450 kHz. The results show that the proposed method greatly outperforms present loss modeling approaches in accuracy and speed, requiring only a limited training dataset. An automatic loss modeling tool based on the new method is provided together with its open-source code.
KW - 2024 OA procedure
KW - Core loss modeling
KW - Knowledge-aware artificial neural network
KW - Machine Learning (ML)
KW - Analytical core loss models
UR - http://www.scopus.com/inward/record.url?scp=85177076366&partnerID=8YFLogxK
U2 - 10.1109/TPEL.2023.3332025
DO - 10.1109/TPEL.2023.3332025
M3 - Article
AN - SCOPUS:85177076366
SN - 0885-8993
VL - 39
SP - 1968
EP - 1973
JO - IEEE Transactions on Power Electronics
JF - IEEE Transactions on Power Electronics
IS - 2
ER -