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Hybrid data-based/model-based inter-turn fault detection methods for PM drives with manufacturing faults

机译:具有制造故障的PM驱动器的基于数据/基于模型的混合匝间故障检测方法

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In this paper, improved online inter-turn fault detection methods are proposed in order to take into account some design specifications related to recent Permanent Magnet Synchronous Machine (PMSM) topologies having modular stator combined with Fractionnal Slot Concentrated Winding (FSCW). This winding allows compact PM motor mainly by reducing end windings. It is often associated with segmented stator in order to simplify the construction of the motor as in the case of Electro-Mechanical Actuators (EMA). This technique may lead to the presence of manufacturing faults such as additional air gaps between the various parts of the modular stator. This urges users to consider complex models of PMSM far removed from classical dq models. Consequently, undesirable harmonics, torque ripples can interfere with fault detection such as inter-turn fault detection algorithms. This paper proposes invasive methods interacting with PMSM control using both model-based and data-based methods in order to take into account these aspects for control and online inter-turn fault detection. Various experimental tests on an industrial EMA prototype validate the effectiveness of the proposed solution.
机译:在本文中,提出了一种改进的在线匝间故障检测方法,以考虑到与具有模块化定子和分数槽集中绕组(FSCW)的最新永磁同步电机(PMSM)拓扑相关的一些设计规范。该绕组主要通过减少端部绕组来实现紧凑型PM电动机。它通常与分段定子相关联,以简化电机的构造,例如在机电执行器(EMA)的情况下。该技术可能导致制造故障的出现,例如模块化定子各个部分之间的额外气隙。这敦促用户考虑将PMSM的复杂模型与传统dq模型相去甚远。因此,不良的谐波,转矩脉动会干扰故障检测,例如匝间故障检测算法。本文提出了使用基于模型和基于数据的方法与PMSM控制交互的侵入性方法,以考虑到控制和在线匝间故障检测的这些方面。在工业EMA原型上进行的各种实验测试验证了所提出解决方案的有效性。

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