Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning
Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning
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DOI：
10.1016/j.engappai.2022.105537
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发表时间：
2021-03
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影响因子：
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通讯作者：
Wilhelm Kirchgässner;Oliver Wallscheid;J. Böcker
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作者：
Wilhelm Kirchgässner;Oliver Wallscheid;J. Böcker
随着电力系统变得越来越紧凑，功率密度越来越高，热应力和基于模型的精确实时热监测的相关性也越来越高。以前的工作热建模的集总参数热网络（LPTNs）遭受强制性的专业知识，他们的设计和所需的功率损耗模型的不确定性。相比之下，基于深度学习的温度模型不能像LPTN那样以相同的估计精度设计少量的模型参数。在这项工作中，介绍了热神经网络（TNN），它统一了基于热传递的LPTNs形式的巩固知识，以及具有监督机器学习的数据驱动的非线性函数逼近。TNN方法通过其状态空间表示具有物理上可解释的状态，克服了先前范例的缺点，通过自动微分框架是端到端可微分的，并且其设计不需要材料、几何形状和专家知识。电动机数据集上的实验表明，TNN实现更高的温度估计精度比以前的白色/灰色或黑箱模型的均方误差为3.18 K2和最坏情况下的错误为5.84 K在64个模型参数。
With electric power systems becoming more compact with higher power density, the relevance of thermal stress and precise real-time-capable model-based thermal monitoring increases. Previous work on thermal modeling by lumped-parameter thermal networks (LPTNs) suffers from mandatory expert knowledge for their design and from uncertainty regarding the required power loss model. In contrast, deep learning-based temperature models cannot be designed with the low amount of model parameters as in a LPTN at equal estimation accuracy. In this work, the thermal neural network (TNN) is introduced, which unifies both, consolidated knowledge in the form of heat-transfer-based LPTNs, and data-driven nonlinear function approximation with supervised machine learning. The TNN approach overcomes the drawbacks of previous paradigms by having physically interpretable states through its state-space representation, is end-to-end differentiable through an automatic differentiation framework, and requires no material, geometry, nor expert knowledge for its design. Experiments on an electric motor data set show that a TNN achieves higher temperature estimation accuracies than previous white-/gray- or black-box models with a mean squared error of 3.18 K2and a worst-case error of 5.84 K at 64 model parameters.