Abstract
The significant advantages of permanent magnet synchronous motors, such as very good dynamic properties, high efficiency and power density, have led to their frequent use in many drive systems today. However, like other types of electric motors, they are exposed to various types of faults, including stator winding faults. Stator winding faults are mainly inter-turn short circuits and are among the most common faults in electric motors. In this paper, the possibility of using the spectral analysis of symmetrical current components to extract fault symptoms and the machine-learning-based K-Nearest Neighbors (KNN) algorithm for the detection and classification of the PMSM stator winding fault is presented. The impact of the key parameters of this classifier on the effectiveness of stator winding fault detection and classification is presented and discussed in detail, which has not been researched in the literature so far. The proposed solution was verified experimentally using a 2.5 kW PMSM, the construction of which was specially prepared for carrying out controlled inter-turn short circuits.
Highlights
The popularity of Permanent Magnet Synchronous Motors (PMSMs) has continued to increase in recent years
The most common situation is that a stator winding fault starts with an inter-turn short circuit, which is very difficult to detect at an early stage
The main goal of this article is inter-turn short-circuit detection and classification in PMSM stator windings using the spectral analysis of symmetrical current components to extract the fault symptoms and a simple Machine Learning (ML)-based classifier (KNN)
Summary
The popularity of Permanent Magnet Synchronous Motors (PMSMs) has continued to increase in recent years This is due to the fact that they are characterized by very good properties such as very high efficiency, high reliability, control of a wide range of rotational speeds and a low rotor moment of inertia [1,2]. The most common faults of electric machines are bearing (41%), stator (36%) and rotor (9%) faults, whereas 14% correspond to other failures [5] This applies to highly efficient and durable PMSMs. The stator winding fault is one of the most common faults of PMSMs. Apart from the wrong connection of windings, stator faults include various types of short circuits (Figure 1): inter-turn short circuits, short circuits between the coils in one phase, phase-to-phase short circuits, phase-to-ground short circuits and open circuits (breaks in phases) [6]. The most common situation is that a stator winding fault starts with an inter-turn short circuit, which is very difficult to detect at an early stage
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