Interturn Short-Circuit Fault Detection of a Five-Phase Permanent Magnet Synchronous Motor

Zhongyi Yang,Yiguang Chen

doi:10.3390/en14020434

Abstract

Interturn short circuits are a common fault of permanent magnet synchronous motors (PMSMs). This paper proposes a new method to detect the interturn short-circuit fault (ISCF) of a five-phase PMSM. The method first takes the command voltage and measured current of each phase winding as the original signal and then obtains the delay signal orthogonal to the original signal via Hilbert transform. Then, the generalized instantaneous reactive power of each phase can be calculated from the orthogonal voltage and current signals of each phase. Finally, the influence of the ISCF on the generalized instantaneous reactive power of each phase is analyzed under different working conditions. By comparing the difference in the generalized instantaneous reactive power of each phase, it can be determined which phase winding has the ISCF. The proposed method is verified by simulated and experimental results.

Highlights

The five-phase permanent magnet synchronous motor (PMSM) has been widely used in fields with high reliability requirements due to its small size, high power density, simple structure, and strong fault tolerance [1,2]
To verify the feasibility of the online diagnosis method for interturn short-circuit fault (ISCF) proposed in this paper, the experimental system shown in Figures 18 and 19 was established
This paper proposed an ISCF detection method based on single-phase generalized instantaneous reactive power

Summary

Introduction

The five-phase permanent magnet synchronous motor (PMSM) has been widely used in fields with high reliability requirements due to its small size, high power density, simple structure, and strong fault tolerance [1,2]. There are three major approaches for the detection of ISCFs in PMSMs. The first approach is the signal-based method, which involves analyzing the selected signal and determining the characteristics of the ISCF [7,8,9]. The first approach is the signal-based method, which involves analyzing the selected signal and determining the characteristics of the ISCF [7,8,9] This method mainly involves stator current analysis, stator current Parker’s vector analysis, and q-axis current analysis. The third approach involves knowledge-based methods using artificial intelligence technology, neural networks, pattern recognition, and other methods to achieve fault monitoring This method provides a fast and accurate simulation of the machine, multiple training algorithms, and the diagnosis of all possible interactions between predictor values. It is very difficult to collect a sufficient amount of data under the considered conditions [19,20]

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Conclusion