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Convergence Investigation of Injection-Based Encoderless Control Algorithms for RSMs in Deep Magnetic Saturation

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Injection-based encoderless control methods are the state-of-the-art solution for estimating the rotor position around zero speed. It is known that stability is a major issue in this category of algorithms.… Click to show full abstract

Injection-based encoderless control methods are the state-of-the-art solution for estimating the rotor position around zero speed. It is known that stability is a major issue in this category of algorithms. Most of these methods become incapable of tracking the rotor position when the machine is driven into deep magnetic saturation. In recent literature, this behavior is often assumed to be a property of the electrical machine. Thus, recent research in this field has focused on the optimization of the electrical machine. The purpose of the following work is to investigate the impact of injection-based encoderless algorithms on the stability issue in deep magnetic saturation. By investigating various algorithms for a reluctance synchronous machine (RSM), it is shown for the first time that the issue results primarily from the algorithm used. One of the investigated algorithms is capable of working without load limitation, confirming the statement that the algorithm is the source of the problem. The reason for this behavior is analyzed using a novel convergence criterion for the RSM, which is derived and verified. A Finite-Element-Method (FEM)-based simulation procedure is proposed to predict the convergence region with high accuracy. This opens new practical relevant possibilities at the design stage of the system. The investigation demonstrates that a deviation between the real and estimated operating point causes the problem. This deviation results in incorrect parameters and thus leads to the instability of the injection-based model.

Keywords: injection; injection based; deep magnetic; based encoderless; magnetic saturation

Journal Title: IEEE Access
Year Published: 2022

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