Axial Position Estimation through an Injection-based Self-sensing Technique for Hybrid Active-Passive Self-bearing Machines
(2023) 2023 IEEE Energy Conversion Congress and Exposition (ECCE) — Location: Nashville, TN, USA (29.October.2023)
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C2023_AxialPositionEstimationthroughanInjection-basedSelf-sensingTechniqueforHybridActive-PassiveSelf-bearingMachine.pdf
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- Abstract
- Self-bearing machines are designed to be the centerpiece of highly integrated magnetically suspended systems by combining the rotor guidance and drive within a single structure. Their compactness and reliability have been taken a step further with the development of self-bearing machines that do not require sensors, controllers and power electronics to guide the rotor. In these passively levitated machines, the suspension of the axial degree of freedom relies on circulating currents that are induced in the armature windings thanks to the permanent magnet motion. The rotor can therefore not be axially stabilised at zero rotational speed. Hybrid actuation approaches, based on the active control of the rotor position until passive operation can be achieved, have been investigated. However, the requirement for a position sensor compromises the advantages of passive levitation. In this context, this paper proposes a self-sensing technique relying on the injection of a high frequency signal to estimate the winding impedance and extract the rotor axial position. Simulations of the rotor dynamics are performed on the basis of an electromechanical model of the machine to validate the operation principle, assess the tracking capabilities and investigate the robustness of this self-sensing technique.
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Citations
Van Verdeghem, J., & Dehez, B. (2023). Axial Position Estimation through an Injection-based Self-sensing Technique for Hybrid Active-Passive Self-bearing Machines. 2023 IEEE Energy Conversion Congress and Exposition (ECCE), Nashville, TN, USA. https://hdl.handle.net/2078.5/28931