Loss Evaluation of an Induction Motor Model Core by Vector Magnetic Characteristic Analysis

Kunihiro, Naoki; Todaka, Takashi; Enokizono, Masato

doi:10.1109/tmag.2010.2072910

Cited by 17 publications

(4 citation statements)

References 6 publications

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“…However, certain factors such as unbalanced power supply voltage, specific types of loads, and instrument errors can engender negative sequence currents even in healthy motors, leading to potential misclassification. Although some studies have considered these effects, the technique remains incapable of detecting faults in induction motors with inherently unbalanced windings, as illustrated in the study [6].…”

Section: Related Workmentioning

confidence: 99%

Fault Diagnosis of Three-Phase Induction Motors Using Convolutional Neural Networks

Badr,

Altawil,

Almomani

et al. 2023

MMEP

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The challenges associated with diagnosing faults in three-phase induction motors necessitate the development of innovative, non-invasive methods that can increase efficiency and reduce costs. This study presents a novel approach to fault detection in these motors, leveraging advanced machine learning technology. The primary focus is the identification of faults related to the stator, including single-phase and three-phase faults, current interruptions, and sudden torque changes. Convolutional Neural Networks (CNN), inspired by the human visual nervous system, form the backbone of the proposed fault detection methodology. This technique utilizes external measurements for processing, circumventing the need for intrusive measures such as opening the motor or installing internal sensors. The non-intrusive nature of this method not only simplifies the process but also significantly reduces associated costs. The CNN-based approach offers superior accuracy in diagnosing faults, facilitating timely prevention measures and potentially saving human lives. It also reduces the time and effort required to identify fault types, thus minimizing motor downtime and associated costs. Simulations were conducted using MATLAB software, and individual fault scenarios were applied and analyzed. The results obtained demonstrate the efficacy of the CNN-based fault diagnosis method, thereby highlighting its potential for implementation in real-world scenarios. This study contributes to the field by providing a detailed exploration of a non-invasive, cost-effective, and highly accurate method for fault detection in three-phase induction motors. It opens avenues for further research into the application of machine learning techniques for fault diagnosis in other types of motors.

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Section: Related Workmentioning

confidence: 99%

Fault Diagnosis of Three-Phase Induction Motors Using Convolutional Neural Networks

Badr,

Altawil,

Almomani

et al. 2023

MMEP

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“…Due to the complicated flux distribution, spatial harmonics, and the rotation of the flux vector, major difficulties are encountered for the last several decades in evaluation of iron loss in the induction motor. Kunihiro et al [1] presented results of iron-loss evaluation of an induction motor model core by using the finite element method (FEM) considering two-dimensional vector magnetic properties. Kwon et al [2] computed the loss distribution of a three-phase IM and a BLDC motor considering operating point-of-core material based on the FEM and experiments.…”

Section: Introductionmentioning

confidence: 99%

Determination of iron loss considering spatial harmonics and tooth pulsation effects for cage motor

Jaiswal¹,

Deshmukh²

2014

2014 International Conference on Renewable Energy Research and Application (ICRERA)

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The contribution of spatial harmonics and tooth pulsation in iron loss of three phase squirrel cage induction motor has been calculated using lumped circuit approach. An equivalent circuit of the three phase induction motor considering stator magnetomotive harmonics has been used for the calculation of the same. An empirical formula has been developed and validated for determination of tooth pulsation loss. The usage of lumped parameter circuit considering magnetomotive force harmonics for predicting the iron loss components has not appeared in the literature. Here iron loss due to fundamental, spatial harmonics components and tooth pulsation for various motors with different designs and polarities have been determined and compared with experimental results. It is found that the iron losses due to the stator magnetomotive harmonics is in same range as that due to fundamental component, the tooth pulsation loss is in the range of 25% to 35% for 2 pole, and 20% to 30% of total iron loss for 4 pole induction motors. The percentage error between computed and average experimental results for total iron loss are within acceptable limits (±5%) without taking too much of computation time compared to the numerical methods.

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“…Complex magnetization mechanisms can take place at laminated iron core of three-phase transformers and in rotating electrical machinery. In such electromagnetic devices alternate losses, rotational losses, and the highly anisotropic nature of electrical steels can be observed and must be taken into consideration for the computation of the magnetic fields evolution (Kunihiro et al, 2011;Kefalas et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Traditionally the calculation of magnetic field in electromagnetic devices design only takes into account the effects of alternating magnetization, while ignoring the effects of rotating magnetization. In fact, the magnetic iron loss caused by rotating magnetization is often larger than the alternating iron loss caused by alternating magnetization of the same induction level (Kunihiro et al, 2011;Kefalas et al, 2011;Enokizono and Okmato, 2002;Takács, 2012.…”

Section: Introductionmentioning

confidence: 99%

Hysteresis parameters estimation using a modified harmony search

Coelho

Mariani

Silva

et al. 2013

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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Purpose -The purpose of this paper is to introduce a chaotic harmony search (CHS) approach based on the chaotic Zaslavskii map to parameters identification of Jiles-Atherton vector hysteresis model. Design/methodology/approach -In laminated magnetic cores when the magnetic flux rotates in the lamination plane, one observes an increase in the magnetic losses. The magnetization in these regions is very complex needing a vector model to analyze and predict its behavior. The vector Jiles-Atherton hysteresis model can be employed in rotational flux modeling. The vector Jiles-Atherton model needs a set of five parameters for each space direction taken into account. In this context, a significant amount of research has already been undertaken to investigate the application of metaheuristics in solving difficult engineering optimization problems. Harmony search (HS) is a derivative-free real parameter optimization metaheuristic algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. In this paper, a CHS approach based on the chaotic Zaslavskii map is proposed and evaluated. Findings -The proposed CHS presents an efficient strategy to improve the search performance in preventing premature convergence to local minima when compared with the classical HS algorithm. Numerical comparisons with results using classical HS, genetic algorithms (GAs), particle swarm optimization (PSO), and evolution strategies (ES) demonstrated that the performance of the CHS is promising in parameters identification of Jiles-Atherton vector hysteresis model. Originality/value -This paper presents an efficient CHS approach applied to parameters identification of Jiles-Atherton vector hysteresis model.

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Loss Evaluation of an Induction Motor Model Core by Vector Magnetic Characteristic Analysis

Cited by 17 publications

References 6 publications

Fault Diagnosis of Three-Phase Induction Motors Using Convolutional Neural Networks

Fault Diagnosis of Three-Phase Induction Motors Using Convolutional Neural Networks

Determination of iron loss considering spatial harmonics and tooth pulsation effects for cage motor

Hysteresis parameters estimation using a modified harmony search

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