Abstract
When a turn-to-turn short fault occurs in an induction motor, it will be accompanied by vibration and heating, which will have adverse effects on the entire power system. Thus, turn-to-turn short fault diagnosis of the stator is required, and major accidents can be prevented if an inter-turn short circuit (ITSC), which is the early stage of a turn-to-turn short, can be detected. This study reinterprets Park’s vector approach using Direct-Quadrature(D-Q) transformation for the linear separation of ITSCs and proposes an ITSC diagnosis method by defining the magnetic flux linkage pulsation and current change in the event of a turn-to-turn short. It is difficult to diagnose because the turn-to-turn short current change in an ITSC is considerably different from the induction motor loss. Hence, it was found through analysis that when the current change is considered through an analysis of the relationship between inductance and the winding number, the ITSC current becomes slightly smaller than the steady-state current. This was verified using the D-Q synchronous reference frame over time. We proposed a linear separation of the ITSC diagnosis from the steady state by considering the minimum values of the pulsating current as feature points.
Highlights
Induction motors are widely used in many industries because they have a simple structure and are applicable to a system [1]
The existing Park’s vector approach (PVA) based on the D-Q synchronous reference frame draws a circular pattern, and a distortion of the circle results when a turn-to-turn short occurs but it is still difficult to diagnose
inter-turn short circuit (ITSC) because it is focused on revealing the distortion of the circular pattern while concentrating on the advantages of simplicity, good visibility, and excellent online diagnosis
Summary
Induction motors are widely used in many industries because they have a simple structure and are applicable to a system [1]. The disadvantage of the mentioned diagnostic signals is difficult processing Because of these inconveniences, the motor current signature analysis (MCSA), which is a sensorless method, is a recent focus of research [16,17,18,19,20,21,22,23]. Use the recognition methods using AItree technology k-nearestsupport neighbors (k-NN), multi-layer perceptron signal source of the voltage or current as (CART), it is, which it difficult to perform linear distinction of (MLP), classification and regression tree andmakes multi-class support vector machine faults due to the noise of the power supply and environmental factors. Turn-to-turn short fault problem of induction motors even conditions and environments of industrial if using somewhat complex method because they can cause major. Flux linkage pulsation, as well as defining the change relation between the ITSC and the current
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