Artificial Intelligence-Aided Thermal Model Considering Cross-Coupling Effects
Artificial Intelligence-Aided Thermal Model Considering Cross-Coupling Effects
复制标题
考虑交叉耦合效应的人工智能辅助热模型
DOI：
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复制
发表时间：
2020
影响因子：
6.7
通讯作者：
F. Blaabjerg
中科院分区：
文献类型：
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作者：
Yi Zhang;Zhongxu Wang;Huai Wang;F. Blaabjerg
这封信提出了一个人工智能辅助的热模型，考虑热交叉耦合效应的电力电子器件/系统。由于多个热源可以同时应用在热系统中，所提出的方法是能够更方便地表征模型参数相比，现有的方法，其中只有一个热源是允许在一个时间。通过采用同步冷却曲线，线性到对数数据重新采样，并区分功率损耗，所提出的基于人工神经网络的热模型可以训练更好的数据丰富性和多样性，同时使用更少的测量。最后，通过实验验证了模型的性能。
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.