• 전력전자학회논문지
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• 제19권2호
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• pp.139-148
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• 2014

State-of-charge (SOC) is one of the significant indicators to estimate the driving range of the electric vehicle and to control the alternator of the conventional engine vehicles as well. Therefore its precise estimation is crucial not only for utilizing the energy effectively but also preventing critical situations happening to the power train and lengthening the lifetime of the battery. However, lead-acid battery is time-variant, highly nonlinear, and the hysteresis phenomenon causes large errors in estimation SOC of the battery especially under the frequent discharge/charge. This paper proposes a novel estimation technique for the SOC of the Lead-Acid battery by using a well-known Extended Kalman Filter (EKF) and an electrical equivalent circuit model of the Lead-Acid battery considering diffusion and hysteresis characteristics. The diffusion is considered by the reconstruction of the open circuit voltage decay depending on the rest time and the hysteresis effect is modeled by calculating the normalized integration of the charge throughput during the partial cycle. The validity of the proposed algorithm is verified through the experiments.

• Nuclear Engineering and Technology
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• 제56권10호
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• pp.4149-4157
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• 2024

Welding processes are used to connect several components in nuclear power plants. These welding processes can induce residual stress in welding joints, which has been identified as a significant factor in primary water stress corrosion cracking. Consequently, the assessment of welding residual stress plays a crucial role in determining the structural integrity of welded joints. In this study, a deep fuzzy neural networks (DFNN) with a rule-dropout method, which is an artificial intelligence (AI) method, was used to predict the residual stress of dissimilar metal welding. ABAQUS, a finite element analysis program, was used as the data collection tool to develop the AI model, and 6300 data instances were collected under 150 analysis conditions. A rule-dropout method and genetic algorithm were used to optimize the estimation performance of the DFNN model. DFNN with the rule-dropout model was compared to a deep neural network method, known as a general deep learning method, to evaluate the estimation performance of DFNN. In addition, a fuzzy neural network method and a cascaded support vector regression method conducted in previous studies were compared. Consequently, the estimation performance of the DFNN with the rule-dropout model was better than those of the comparison methods. The welding residual stress estimation results of this study are expected to contribute to the evaluation of the structural integrity of welded joints.

• 대한기계학회논문집B
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• 제24권12호
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• pp.1625-1634
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• 2000

Verification of measurements is required for precise evaluation of turbine cycle performance in nuclear power plant. We assumed that initial acceptance data and design data of the plant could provide correlation information between performance data. The data can be used as sample sets for the correct estimation model of measurement value. The modeling was done practically by using regression model based on plant design data, plant acceptance data and verified plant performance data of domestic nuclear power plant. We can construct more robust performance analysis system for an operation nuclear power plant with this validation scheme.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2010년도 제39회 하계학술대회 초록집
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• pp.74-74
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• 2010

The aqua-plasma is the non-thermal plasma in electrical conductive electrolyte by generates the vapor film layer on the immersed metal electrode surface. This plasma can generate the hydroxyl radical by dissociate the water molecule with the plasma electron. To develop the plasma discharge device for high efficiency in the hydroxyl radical generation, proper model for estimation of plasma power is necessary. In this work, the 1-D spherical model was developed, considering temperature dependence material constants. The relation between the plasma power and hydroxyl generation was also studied by the comparison between the optical emission intensity from the hydroxyl radical using monochromator and estimated plasma power. First, the thickness of vapor layer thickness was estimated using the Navier-Stokes fluid equation in order to calculate the discharge E-field inside vapor layer. Using the E-field magnitude and power balance on the plasma generation, it was possible to estimate the plasma power. The plasma power was assumed to uniformly fill the vapor layer and the temperature of vapor layer was fixed in the boiling temperature of electrolyte, 375K. In the experiment, the aqua-plasma was discharged in the saline by applied the voltage on the bipolar electrode. The range of applied voltage was 234 to 280V-rms in the frequency of 380 kHz. Two type electrodes were produced with two ${\Phi}0.2$ tungsten. The plasma power was estimated from the V-I signal from the two high voltage probes and current probe. The estimated plasma power agreed with the profile of emission intensity when the plasma discharged between the metal electrode and vapor layer surface. However, when the plasma discharged between the metal electrodes, the increasing rate of emission intensity was lower than the increase of plasma power. It implies that the surface reaction is more sufficient rather than the volume reaction in the radical generation, due to the high density of water molecule in the liquid.

• Nuclear Engineering and Technology
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• 제53권3호
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• pp.860-865
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• 2021

A data-based model, such as an AAKR model is widely used for monitoring the drifts of sensors in nuclear power plants. However, since a training dataset and a test dataset for a data-based model cannot be constructed with the data from all the possible states, the model uncertainty cannot be good enough to represent the uncertainty of estimations. In fact, the errors of estimation grow much bigger if the incoming data come from inexperienced states. To overcome this limitation of the model uncertainty, a new measure of uncertainty for a data-based model is developed and the predicted uncertainty is introduced. The predicted uncertainty is defined in every estimation according to the incoming data. In this paper, the AAKR model is used as a data-based model. The predicted uncertainty is similar in magnitude to the model uncertainty when the estimation is made for the incoming data from the experienced states but it goes bigger otherwise. The characteristics of the predicted model uncertainty are studied and the usefulness is demonstrated with the pressure signals measured in the flow-loop system. It is expected that the predicted uncertainty can quite reduce the false alarm by using the variable threshold instead of the fixed threshold.

• 전력전자학회논문지
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• 제17권3호
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• pp.222-229
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• 2012

The SOC estimation method based on Kalman Filter(KF) requires the accurate battery model to express the electrical characteristics of the battery. However, the performance of KF SOC estimator can hardly be improved because of the nonlinear characteristic of the battery. This paper proposes the new KF SOC estimator of Lithium-Polymer Battery(LiPB), which considers the variation of parameters based on the hysteresis effect, the magnitude of SOC, the charging/discharging mode and the on/off load conditions. The proposed SOC estimation method is verified with the PSIM simulation combined the experimental data of the LiPB.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 C
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• pp.1188-1191
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• 1998

Detailed estimation of subsurface resistivity distribution and accurate estimation of actual fault current coming into the grounding system are indispensible to optimun grounding system design. Especially, it is essential for efficient grounding design to estimate subsurface resistivity distribution quantitatively and logically. Accurate estimation of subsurface resistivity distribution has an absolute influence on calculating touch voltage, step voltage and ground potential rise (GPR) which are related with grounding design standard for human safety. In this study, thirty-three electrical sounding surveys were made in Yongdam Power Station to obtain detailed subsurface resistivity distribution and the sounding data were interpreted quantitatively using multi-layered model. The results of the quantitative resistivity models were adopted practically to calculate grounding resistance values. Analytical asymptotic equations and CDEGS program were used in grounding resistance calculation and the results were compared and reviewed in the study.

• 전력전자학회논문지
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• 제15권4호
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• pp.309-318
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• 2010

본 논문은 풍력 터빈에서 발생되는 플리커를 추정하기 위하여 SVR(Support Vector Regression)을 이용한 시뮬레이션 모델을 제시한다. SVR 훈련을 위한 입력은 전압 변동이고 출력은 플리커로 설정한다. 오프라인 훈련을 통해 출력전압과 플리커의 관계가 도출된다. 제안된 기법은 플리커를 추정하는데 필요한 데이터 양을 줄이고 또 연산시간을 감소시킨다. 제안된 알고리즘은 시뮬레이션과 실험을 통해 그 타당성이 검증된다.

• Journal of Power Electronics
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• 제17권2호
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• pp.398-410
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• 2017

This work presents the design of a dual extended Kalman filter (EKF) as a state/parameter estimator suitable for adaptive state-of-charge (SoC) estimation of an automotive lithium-iron-phosphate ($LiFePO_4$) cell. The design of both estimators is based on an experimentally identified, lumped-parameter equivalent battery electrical circuit model. In the proposed estimation scheme, the parameter estimator has been used to adapt the SoC EKF-based estimator, which may be sensitive to nonlinear map errors of battery parameters. A suitable weighting scheme has also been proposed to achieve a smooth transition between the parameter estimator-based adaptation and internal model within the SoC estimator. The effectiveness of the proposed SoC and parameter estimators, as well as the combined dual estimator, has been verified through computer simulations on the developed battery model subject to New European Driving Cycle (NEDC) related operating regimes.

• 한국자동차공학회논문집
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• 제22권3호
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• pp.130-135
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• 2014

In this paper, a new estimation method is proposed to calculate steering rack axial force using a 3 dimensional tire mesh model when a car is standing on the road. This model is established by considering changes of camber angle and contact patch between the tires and the ground according to steering angle. The steering rack bar axial force is estimated based on the static equilibrium equations of forces and moments. A tire friction force is supposed to act on the center point of the contact patch, and the proportional coefficient of friction depending on contact patch is suggested. Using the proposed estimation method, rack axial force sensitivity analysis is evaluated according to changes of suspension geometry. Then optimal motor power of Motor Driven Power Steering(MDPS) is evaluated using suggested rack forces.