Sensorless Saliency Extraction using Quadratic-Regression-based Current Derivative Estimation

E. R. Montero, M. Vogelsberger, W. Teppan, T. Wolbank
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Abstract

Stable field oriented control of induction machines in the proximity of zero electrical frequency relies on the extraction of machine saliencies for rotor flux/positon acquisition. To obtain such saliency information, voltage step excitation methods can be used. They excite the machine with a voltage step caused by the inverter and calculate the resulting phase current derivative, which contains several terms including the superposition of saliency components. Multiple strategies have been used to calculate the current derivative, such as FFT, neural networks, or linear regression. However, they do not take into account the influence of a curvature of the current response. This paper proposes using a least-square quadratic regression to calculate machine saliency information. In this sense, the linear response of the phase current can be accurately isolated from the inherent curvature. It will be proved by experimental measurements that the different saliencies' components are observed in both the second order and also first order term of the quadratic regression function. A performance comparison between linear regression and quadratic regression will be shown in terms of saliency acquisition.
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基于二次回归的电流导数估计的无传感器显著性提取
感应电机在零频率附近的稳定磁场定向控制依赖于提取电机显著性以获取转子磁链/位置。为了获得这种显著性信息,可以使用电压阶跃激励方法。他们用逆变器引起的电压阶跃激励机器,并计算得到的相电流导数,其中包含几个项,包括显著分量的叠加。多种策略已被用于计算当前的导数,如FFT,神经网络,或线性回归。然而,它们没有考虑当前响应曲率的影响。本文提出用最小二乘二次回归计算机器显著性信息。从这个意义上说,相电流的线性响应可以准确地从固有曲率中分离出来。通过实验测量证明,在二次回归函数的二阶项和一阶项中都观察到不同的显著性成分。将在显著性获取方面对线性回归和二次回归进行性能比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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