Demagnetization Monitoring and Identification in PM Generators With Concentrated Windings During Transient Conditions

Gyftakis, Konstantinos N.; Garcia-Calva, Tomas Alberto; Skarmoutsos, G. A.; Moriñigo-Sotelo, Daniel; Mueller, Markus; Romero-Troncoso, René de Jesús

doi:10.1109/tia.2022.3221699

Cited by 8 publications

(2 citation statements)

References 31 publications

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“…Several methods have been reported for diagnosing short-circuit faults, with machine current signature analysis being the most popular method [3][4][5]. In addition, the winding function approach [6][7][8], finite element approach [9,10], frequency signature analysis [11,12], frequency response analysis [13,14], and sound analysis [15] have been studied.…”

Section: Introductionmentioning

confidence: 99%

Identification System for Short-Circuit Fault Points in Concentrated Stator Windings of Motors

Nakamura,

Mizuno

2024

Energies

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Motors serve as the primary power sources in a wide range of industrial fields. In recent years, their application has been expanded to electric and hybrid electric vehicles. As the performance of the motors installed in electric vehicles directly affects human life, it is critical to diagnose the condition of the windings. The objective of this article is to establish a method to identify the short-circuit fault points in concentrated stator windings based on the magnetic flux density distribution near the stator windings. Unlike with distributed windings, the coils are wound around the teeth in concentrated windings. Thus, it is expected that the accurate position specification of the short circuit can be realized if a detailed magnetic flux density distribution over the teeth is obtained with an appropriate magnetic field sensor. The problem of sensor positioning is solved with two stepper motors moving the search coil in the rotational and longitudinal directions independently at specified intervals. The excellent capability of the proposed system is verified through experiments using the stator winding employed in hybrid electric vehicles. The accuracy and sensitivity of the proposed identification system for short-circuit fault points may enable its practical application in industries, for example, shipping and periodic inspections as well as the production management of motors with concentrated stator windings.

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Section: Introductionmentioning

confidence: 99%

Identification System for Short-Circuit Fault Points in Concentrated Stator Windings of Motors

Nakamura,

Mizuno

2024

Energies

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“…In addition, demagnetization, together with angular, axial, static, and dynamic eccentricity. Gyftakis et al [33] proposed a method based on higher harmonics of the stator current spectrum. The proposed method can separate demagnetization from eccentricity and short-circuit faults while taking into account transient machine conditions.…”

Section: Introductionmentioning

confidence: 99%

The Neutral Voltage Difference Signal as a Means of Investigating Eccentricity and Demagnetization Faults in an AFPM Synchronous Generator

Barmpatza

2023

Machines

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This article investigates the neutral voltage difference signal, VNO signal, for fault diagnosis. The aforementioned signal is the signal of the voltage between the common star point of the stator and the common star point of the load. The under-study faults are demagnetization and static eccentricity faults, while the machine in which the faults are investigated is an axial flux permanent magnet (AFPM) synchronous generator, suitable for wind power applications. This study was conducted using a 3D finite element method (3D-FEM), and the machine’s FEM model was validated through experiments. This method is one of the most accurate methods for electrical machine computation, allowing for a detailed study of electromagnetic behavior. The components that constitute the VNO signal were determined using a 3D-FEM software program (Opera 18R2). Subsequently, further analysis was performed using MATLAB R2022b software, and a fast Fourier transform (FFT) was applied to this signal. In all the investigated faulty cases, new harmonics appeared, and the healthy amplitudes of most of the already existing harmonics increased. These findings can be used for fault identification. The analysis revealed that the harmonic frequency of 1.5fs was the most dominant in the case of demagnetization, while in the case of static eccentricity, the most dominant harmonic was a frequency equal to the machine’s operating frequency, fs. The novelty of this study is that this signal has not previously been used for fault identification, especially in AFPM synchronous machines. This signal depends on EMF voltage and stator phase currents but is less sinusoidal. Consequently, it can detect faults in cases where the aforementioned signals cannot be used for detection.

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Demagnetisation fault analysis and diagnosis based on different methods in permanent magnet machines ‐ An overview

Faiz,

Bazrafshan,

Tabarniarami

2024

IET Electric Power Appl

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The expansion of permanent magnet (PM) machines due to functional advantages such as power density and low losses (high efficiency) and better controllability in industrial applications has led to an increased need for research on possible faults in this type of machine and the development of effective methods to diagnose faults in different operational conditions. The performance characteristics of PM machines are strongly influenced by the PMs used in their structure. Accordingly, one of the most important PM machine faults is the demagnetisation fault which arises due to the physical, operational, and environmental conditions or their combination. This can result in damage to the functional characteristics of the machine. In this paper, the main topologies of PM machines, including the PM synchronous machines, the brushless DC motors, and the flux modulation machines, are first reviewed. Subsequently, the techniques for detecting demagnetisation faults in PM machines are then reviewed and summarised. This paper discusses the factors that cause faults in PM machines and their effects on machine performance. It also examines conventional modelling methods used to study demagnetisation faults. Finally, it presents a general structure for techniques developed for demagnetisation faults in different operating conditions (stationary and non‐stationary) and based on intelligent methods. It also compares the proposed methods and highlights their strengths and weaknesses.

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Demagnetization Monitoring and Identification in PM Generators With Concentrated Windings During Transient Conditions

Cited by 8 publications

References 31 publications

Identification System for Short-Circuit Fault Points in Concentrated Stator Windings of Motors

Identification System for Short-Circuit Fault Points in Concentrated Stator Windings of Motors

The Neutral Voltage Difference Signal as a Means of Investigating Eccentricity and Demagnetization Faults in an AFPM Synchronous Generator

Demagnetisation fault analysis and diagnosis based on different methods in permanent magnet machines ‐ An overview

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