• Journal of Welding and Joining
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• v.13 no.3
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• pp.77-88
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• 1995

A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermomechanical analysis has been performed for the CO$_{2}$ arc welding using the finite element method. The first part of numerical analysis performs a three-dimensional transient heat transfer analysis, and the second part then uses the results of the first part and performs a three-dimensional transient thermo-elastic-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method are used to train a backpropagation neural network to predict the residual stress. Architecturally, the fully interconnected network consists of an input layer for the voltage and current, a hidden layer to accommodate the ailure mechanism mapping, and an output layer for the residual stress. The trained network is then applied to the prediction of residual stress in the four specimens. It is concluded that the accuracy of the neural network predicting method is fully comparable with the accuracy achieved by the traditional predicting method.

• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.51-60
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• 2001

A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermo-mechanical analysis has been performed for the $CO_2$ arc welding using the finite element method. The first part of numerical analysis performs a three-dimensional transient heat transfer analysis, and the second part then uses the results of the first part and performs a three-dimensional transient thermo-elastic-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method are used to train a back propagation neural network to predict the residual stress. Architecturally, the fully interconnected network consists of an input layer for the voltage and current, a hidden layer to accommodate the failure mechanism mapping, and an output layer for the residual stress. The trained network is then applied to the prediction of residual stress in the four specimens. It is concluded that the accuracy of the neural network predicting method is fully comparable with the accuracy achieved by the traditional predicting method.

• International Journal of Safety
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• v.1 no.1
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• pp.36-42
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• 2002

This paper is aimed at predicting the life of rubber insulating gloves under normal operating stresses from relatively rapid test performed at higher stresses. Specimens of rubber insulating gloves are subject to multiple stress conditions, i.e. combined electrical and thermal stresses. Two modes of electrical stress, step voltage stress and constant voltage stress are used in specimen aging. There are two types of test for electrical stress in this experiment: the one is Breakdown Voltage (BDV) test under step voltage stress and thermal stress and the other is lifetime test under constant voltage stress and temperature stress. The ac breakdown voltage defined as the break-down point of insulation that leakage current excesses a limit value, l0mA in this experiment, is determined. Because the very high variability of aging data requires the application of statistical model, Weibull distribution is used to represent the failure times as the straight line on Weibull probability paper. Weibull parameters are deter-mined by three statistical methods i.e. maximum likelihood method, graphical method and least squares method, which employ SAS package, Weibull probability paper and FORTRAN, respectively. Two chosen models for predicting the life under simultaneous electrical and thermal stresses are inverse power model and exponential model. And the constants of life equation for multistress aging are calculated using numerical method, such as Gauss Jordan method etc.. The completion of life equation enables to estimate the life at normal stress based on the data collected from accelerated aging test. Also the comparison of the calculated lifetimes between the inverse power model and the exponential model is carried out. And the lifetimes calculated by three statistical methods with lower voltage than test voltage are compared. The results obtained from the suggested experimental method are presented and discussed.

• Journal of Power Electronics
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• v.14 no.5
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• pp.859-865
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• 2014

This study presents an electromagnetic model for predicting shaft voltages in a 2-pole field-excited synchronous generator. After the first observations on shaft voltages were made more than a century ago, extensive work has been conducted on eliminating, mitigating, and integrating the aforementioned phenomena. Given that emphasis has been placed on modeling shaft- and bearing-induced voltages in AC motors driven by variable frequency drives, similar efforts toward a model that is dedicated to generators are insubstantial. This work endeavors to improve current physical interpretation and prediction methods for shaft-induced voltages in generators through semi-analytical derivation. Aside from the experimental validation of the model, investigations regarding the behavior of shaft voltages under varying machine complexities and operating conditions clarify previous uncertainties regarding these phenomena. The performance of the numerical method is also assessed for application in eccentricity fault diagnosis.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1058-1065
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• 2015

This paper present a current predicting control method for PMSM (permanent magnet synchronous motor) to improve the tracking performance of stator current, which regards the current vector as the control target. Solving the model state equation in the static frame (α-β frame), the dynamic change of current vector will be gained as three independent terms. These change terms, which contain the prediction of current vector, are discretized and simplified by Taylor series expansion and used to get the voltage vector as the predictive control quantity. SVPWM will transform the control voltage to the switching signal of inverter, which is newly deduced for the current vector. Simulation and experiment results are given to testy and verify the performance of this method.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.95-97
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• 1994

This paper describes a method of predicting current, magnetic flux, inductance and torque of switched reluctance motor(SRM) using finite element method with time step considering exciting voltage.

• Journal of Power Electronics
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• v.17 no.1
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• pp.190-199
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• 2017

In this paper, a predictive direct power control (DPC) method based on a Kalman filter is presented for three-phase pulse-width modulation (PWM) rectifiers to improve the performance of rectifiers with source voltages that are distorted with harmonic components. This method can eliminate the most significant harmonic components of the source voltage using a Kalman filter algorithm. In the process of predicting the future real and reactive power to select an optimal voltage vector in the predictive DPC, the proposed method utilizes source voltages filtered by a Kalman filter, which can mitigate the adverse effects of distorted source voltages on control performance. As a result, the quality of the source currents synthesized using the PWM rectifier is improved, and the total harmonic distortion (THD) values are reduced, even under distorted source voltages.

• Proceedings of the KIPE Conference
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• 2007.11a
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• pp.212-214
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• 2007

Regenerative energy which is generated during brake periods of DC traction might cause malfunction or destruction of rectifier or any other power conversion devices caused the increment of DC line voltage. Regenerative energy storage system using super-capacitor is one of the method to control the DC line voltage safely. And super-capacitor is very important device as energy storage device. Therefore, In this paper, we designed the regenerative energy storage system using super-capacitor and propose the method about predicting the lifetime of super-capacitor established in storage system. According to the this research, we can estimate the proper replacement moment for the existed super-capacitor due to the safety of the system. And improve the reliability of regenerative energy storage system using super-capacitor.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.1
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• pp.37-42
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• 2000

The radio noise produced by corona discharge in high voltage transmission lines is one of the most important line design considerations. Therefore it is necessary for transmission line designers to pre-evaluate radio noise using prediction formulas or field test results. In this paper, more accurate and useful formulas for predicting radio noise during fair and foul weathers in high voltage AC transmission lines were proposed through comparison with the existing formulas. Also it was verified by comparing with the long-term measured data from operating lines that the proposed formulas are more accurate. The proposed prediction formulas are developed by the applications of nonlinear least squares optimization method to radio noise database collected from lines throughout the world.

• Journal of the Korean Society of Safety
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• v.11 no.3
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• pp.75-80
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• 1996

This paper presents a study on characteristics and safety for human body in ELF electric and magnetic fields using statistical method. The magnetic fields from a power line can be computed given a knowledge of the currents, voltage and geometry of the line. In this paper, a statistical method for predicting the magnetic fields given the inherent indetermination of the currents is presented. But the electric field is calculated given a knowledge of the voltage and geometry of the line. The effect of unexpected fluctuations in current is modeled by the Monte Carlo simulation. The suggested method is applied to the 345kV and 765kV transmission line system, the result shows that the maximum electric and magnetic field intensity is 6.8627kV/m and 284mG in 345kV system, 2. 5590kV/m and 35mG in 765kV system, respectively.