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Gray-Box Loss Model for Induction Motor Drives

机译:感应电动机驱动器灰度箱损耗模型

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摘要

Both high-precision and high-efficient torque control of induction motor drives is an important research field due to the extensive use of these motors in torque-controlled applications, e.g. electric vehicles. To achieve high precision, a gray-box rotor flux observer and inverter model have been lately introduced and validated to be effective. Gray-box models (GBMs) combine first-order principles from physics with data-driven identification enabling accurate model performances at low model complexity. Since the effectiveness of an operating strategy significantly depends on the accuracy of the underlying loss model, in this paper, the aforementioned GBMs’ scope is extended to estimate also the power losses in the motor and inverter. Hence, the achieved universal drive model delivers flux, torque, and loss estimations which are substantial for various control tasks, like torque control, operating strategy, or thermal models. In comprehensive test bench investigations, which take into account the entire drive operating range, the measured power losses are compared to the model estimations. This analysis validates that the GBMs can estimate the losses in the motor with a root-mean-square error of 0.47 % and in the inverter with 0.87 %, both related to the nominal mechanical power of the motor.
机译:由于扭矩控制应用中的这些电机广泛使用,因此,感应电动机驱动的高精度和高效扭矩控制是一个重要的研究领域,例如,这些电机广泛使用。电动车。为了实现高精度,最近介绍了灰盒转子通量观测器和逆变器模型并验证以有效。灰度盒型号(GBMS)将一阶原理从物理学结合起来,通过数据驱动识别,实现了低模型复杂性的准确模型性能。由于操作策略的有效性显着取决于底层损失模型的准确性,因此在本文中,延长了上述GBMS的范围,以估计电机和逆变器中的功率损耗。因此,实现的通用驱动模型可提供磁通,扭矩和损耗估计,这对于各种控制任务,如扭矩控制,操作策略或热模型。在综合测试台调查中,考虑到整个驱动器操作范围,将测量的功率损耗与模型估计进行比较。该分析验证了GBMS可以在电动机中的损耗估计,具有0.47%的根均方误差和0.87%,与电动机的标称机械功率有关。

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