Investigation of artificial neural networks for estimating important component temperatures in electric motors

研究用于估计电动机重要部件温度的人工神经网络

基本信息

批准号：
388765580
负责人：
Professor Dr.-Ing. Joachim Böcker
金额：
--
依托单位：
Fachgebiet Leistungselektronik und Elektrische Antriebstechnik (LEA)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2017
资助国家：
德国
起止时间：
2016-12-31 至 2021-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/388765580?language=en
关键词：
Investigation artificial neural networks estimating

项目摘要

The model-based estimation of important component temperatures in electric motors is an important research subject in recent years. It is used for component protection during run-time and thus forms an important basis for increasing the degree of thermal utilization regarding modern drive systems. Typically, indirect methods based on electrical motor models can be used to detect temperature-sensitive parameter changes and thus to observe the operating temperature. This model class has the inherent disadvantage that the so-called hot-spot temperature cannot be estimated, and that some important engine temperatures (e.g., ball bearings) cannot be detected. On the other hand, pumped parameter thermal networks (LPTN) are frequently used to directly model the temperature distribution. There is a wide range with regard to the LPTN modeling depth. For real-time capable models, particularly abstracted approaches are used to estimate the relevant motor temperatures with a model structure that is as compact as possible and thus computationally efficient. The question here is whether alternative model approaches are no better suited for empirical-abstract access to temperature estimation within complex systems. By avoiding LPTN-based differential equations, it is then possible in to select the number of degrees of freedom of the model independently of the number of components to be modeled. As a result, the estimation accuracy against abstract LPTN approaches could be further increased with a reasonable additional calculation effort.Artificial neural networks (KNN) comprise a wide range of different black-box models and already find a wide range of applications, e.g. in speech and image analysis. These have been completely neglected for the present application, so that the applicant has carried out an initial preliminary investigation into the basic suitability of KNN. Here it could be shown that topologies using long-short-term-memories or gated recurrent units provide promising estimation accuracy. However, they are still below the established direct and indirect methods. In this investigation, numerous open research questions have been identified, e.g. the problem of hyper-parameter optimization. These are superordinate configuration parameters of the considered KNN topology (for example, the number of hidden layers or number of neurons per layer) or the training algorithm used (for example, initialization of the KNN weights). This project is therefore aimed at the systematic investigation of KNN for the temperature estimation in electric motors, whereby a general methodological and process chain is developed so that the project results can be directly transferred to related technical systems, e.g. to batteries or power-electronic converters.

近年来，基于模型的电动机中重要组件温度的估计是重要的研究主题。它用于在运行时间内用于组件保护，因此构成了增加现代驱动系统的热利用程度的重要基础。通常，基于电机模型的间接方法可用于检测对温度敏感的参数变化，从而观察工作温度。该模型类具有固有的缺点，即无法估算所谓的热点温度，并且无法检测到一些重要的发动机温度（例如，球轴承）。另一方面，泵送参数热网络（LPTN）经常用于直接建模温度分布。在LPTN建模深度方面有很广泛的范围。对于实时模型，使用特别抽象的方法来估计具有尽可能紧凑且因此在计算上有效的模型结构的相关运动温度。这里的问题是，替代模型方法是否不太适合于在复杂系统内对温度估计的经验易位。通过避免基于LPTN的微分方程，就可以独立于对要建模的组件的数量独立选择模型的自由度数量。结果，通过合理的额外计算工作，可以进一步提高针对抽象LPTN方法的估计精度。人工神经网络（KNN）构成了广泛的不同黑盒模型，并且已经找到了广泛的应用，例如。在语音和图像分析中。这些对本申请被完全忽略了，因此申请人对KNN的基本适合性进行了初步的初步研究。在这里，可以表明，使用长期记忆或封闭式复发单元的拓扑提供了有希望的估计精度。但是，它们仍低于已建立的直接和间接方法。在这项调查中，已经确定了许多开放研究问题，例如高参数优化的问题。这些是所考虑的KNN拓扑的上级配置参数（例如，隐藏层的数量或每层神经元的数量）或所使用的训练算法（例如，KNN权重的初始化）。因此，该项目的目的是针对电动机温度估计的KNN进行系统的研究，从而开发了一般的方法论和过程链，以便可以将项目结果直接传输到相关的技术系统，例如进行电池或电源转换器。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Deep Residual Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous Motors

DOI：
10.1109/iemdc.2019.8785109
发表时间：
2019-01-01
期刊：
2019 IEEE INTERNATIONAL ELECTRIC MACHINES & DRIVES CONFERENCE (IEMDC)
影响因子：
0
作者：
Kirchgaessner, Wilhelm;Wallscheid, Oliver;Boecker, Joachim
通讯作者：
Boecker, Joachim

Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations

用神经常微分方程学习电动机的热特性和温度模型

DOI：
10.23919/ipec-himeji2022-ecce53331.2022.9807209
发表时间：
2022
期刊：
2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia)
影响因子：
0
作者：
W. Kirchgässner;O. Wallscheid;J. Böcker
通讯作者：
J. Böcker

Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors With Supervised Machine Learning: A Benchmark

具有监督机器学习的同步电机中数据驱动的永磁体温度估计：基准