This paper, starting from recent papers in the scientific literature dealing with Rotating Induction Motor (RIM) dynamic modelling, as a first step, improves its space-vector dynamic model, including both the magnetic saturation and iron losses; The main original aspects of the proposed model are the following: 1) the magnetic saturation of the iron core has been described on the basis of both current versus flux and flux versus current functions, 3) it includes the iron losses, separating them in hysteresis and eddy current ones, 4) it includes the effect of the load on the magnetic saturation. Afterwards, it proposes an off-line technique for the estimation of electrical parameters of this model, which is based on Genetic Algorithms (GA). The proposed method is based on input-output measurements and needs neither the machine design geometrical data nor a Finite Element Analysis (FEA) of the machine. It focuses on the application of an algorithm based on the minimization of a suitable cost function depending on the stator current error. The proposed electrical parameters estimation method has been initially tested in numerical simulation and further verified experimentally on a suitably developed test set-up.

中文翻译：

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基于GA的感应电动机模型的离线参数估计，包括磁饱和和铁损

作为第一步，本文从科学文献中有关旋转感应电动机（RIM）动态建模的最新论文开始，改进了其空间矢量动态模型，包括磁饱和和铁损。提出的模型的主要原始方面如下：1）已根据电流与磁通量以及磁通与电流函数的关系描述了铁芯的磁饱和，3）包括铁损，将其磁滞分离和涡流电流； 4）它包括负载对磁饱和的影响。此后，它提出了一种基于遗传算法（GA）的离线技术，用于估算该模型的电参数。所提出的方法基于输入-输出测量，并且不需要机器的设计几何数据也不需要机器的有限元分析（FEA）。它着重于基于定子电流误差最小化合适成本函数的算法的应用。所提出的电参数估计方法已经在数值模拟中进行了初步测试，并在适当开发的测试装置上进行了实验验证。