TY - JOUR
T1 - Artificial Intelligence-Aided Thermal Model Considering Cross-Coupling Effects
AU - Zhang, Yi
AU - Wang, Zhongxu
AU - Wang, Huai
AU - Blaabjerg, Frede
N1 - Funding Information:
Manuscript received January 18, 2020; revised February 19, 2020; accepted March 4, 2020. Date of publication March 11, 2020; date of current version June 23, 2020. This work was supported by the Innovation Fund Denmark through the Advanced Power Electronic Technology and Tools (APETT) under Project 877302. (Corresponding author: Zhongxu Wang.) The authors are with the Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark (e-mail: [email protected]; [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 1986-2012 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - This letter proposes an artificial intelligence-aided thermal model for power electronic devices/systems considering thermal cross-coupling effects. Since multiple heat sources can be applied simultaneously in the thermal system, the proposed method is able to characterize model parameters more conveniently compared to existing methods where only single heat source is allowed at a time. By employing simultaneous cooling curves, linear-to-logarithmic data re-sampling, and differentiated power losses, the proposed artificial neural network-based thermal model can be trained with better data richness and diversity while using fewer measurements. Finally, experimental verifications are conducted to validate the model capabilities.
AB - This letter proposes an artificial intelligence-aided thermal model for power electronic devices/systems considering thermal cross-coupling effects. Since multiple heat sources can be applied simultaneously in the thermal system, the proposed method is able to characterize model parameters more conveniently compared to existing methods where only single heat source is allowed at a time. By employing simultaneous cooling curves, linear-to-logarithmic data re-sampling, and differentiated power losses, the proposed artificial neural network-based thermal model can be trained with better data richness and diversity while using fewer measurements. Finally, experimental verifications are conducted to validate the model capabilities.
KW - Artificial intelligence
KW - power electronic devices and systems
KW - thermal cross-coupling effects
KW - thermal modeling
UR - http://www.scopus.com/inward/record.url?scp=85087784439&partnerID=8YFLogxK
U2 - 10.1109/TPEL.2020.2980240
DO - 10.1109/TPEL.2020.2980240
M3 - Article
AN - SCOPUS:85087784439
SN - 0885-8993
VL - 35
SP - 9998
EP - 10002
JO - IEEE Transactions on Power Electronics
JF - IEEE Transactions on Power Electronics
IS - 10
M1 - 9034112
ER -