• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.286-289
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• 2000

from the viewpoint of model-based current control it is indispensable to use the accurate system parameters for the high control performance. This paper adopts the Least-Squares algorithm as a parameter estimation scheme because it has the fast convergence rate and the low sensitivity to noises. in case of the PI current controller with high gains the simulation results show that the adopted estimation scheme can be successfully applied to PWM converters and also show that the control performance can be improved by using the estimated parameters.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.149-152
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• 1998

From the viewpoint of model-based current control, it is indispensable to use the accurate system parameters for the high control performance. This paper adopts the Least-Squares algorithm as a parameter estimation scheme because it has the fast convergence rate and the low sensitivity to noises. In case of the intelligent current controller with delay compensator, the simulation results show that the adopted estimation scheme can be successfully applied to PWM converters and also show the improved control performance in the estimated parameters.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.427-429
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• 2005

In order to tune the speed controller in the linear servo applications the accurate information of a mover mass including a load mass is always required. This paper suggests the mass estimation method of a permanent magnet linear synchronous motor(PMLSM) by using the parameter estimation method of Least-Squares algorithm. First, the deterministic autoregressive moving average(DARMA) model of the mechanical dynamic system is derived. The application of the Least-Squares algorithm shows that the mass can be accurately estimated both in the simulation results and in the experimental results.

• Proceedings of the KIEE Conference
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• summer
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• pp.1525-1527
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• 2004

Modeling of the battery for 42V Power-Net system is presented. For the Battery Management System(BMS) algorithm in a Mildhybrid vehicle, accuracy in SOC estimation is crucial. The battery model is needed for the BMS algorithm as well as system computer symulation for the energy management. The battery model was composed of impedance elements and the each element of the model is estimated by the analysis of the terminal voltage. The result of the model is confirmed by experimental data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.7
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• pp.1007-1013
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• 2012

Modeling of photovoltaic power systems is significant to analytically predict its dynamics in practical applications. This paper presents a novel modeling algorithm of such system by using probability and statistic theories. We first establish a linear model basically composed of Fourier parameter sets for mapping the input/output variable of photovoltaic systems. The proposed model includes solar irradiation and ambient temperature of photovoltaic modules as an input vector and the inverter power output is estimated sequentially. We deal with these measurements as random variables and derive a parameter learning algorithm of the model in terms of statistics. Our learning algorithm requires computation of an expectation and joint expectation against solar irradiation and ambient temperature, which are analytically solved from the integral calculus. For testing the proposed modeling algorithm, we utilize realistic measurement data sets obtained from the Seokwang Solar power plant in Youngcheon, Korea. We demonstrate reliability and superiority of the proposed photovoltaic system model by observing error signals between a practical system output and its estimation.

• Environmental and Resource Economics Review
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• v.9 no.5
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• pp.903-924
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• 2000

This paper examines the alternative method to measure the state of overall economic activity. The macroeconomic variables, used for business cycle, take more than a month after a period for collection and aggregation. The electricity generation data is compiled in mechanical ways just after the period. Based on this fact, we develop the two stage estimation method for coincident economic indicators in order to detect the business cycle in an earlier period, using Stock-Watson's Dynamic Factor Model. Using monthly data from 1970 to 1999, it is found that the experimental coincidence economic indicators are well-fitted to data and also that the estimates of two stage estimation method have good explanatory power, equivalent to the experimental coincidence economic indicators. While the RMSE of coincidence economic indicators is found to be 1.27%, that of the experimental coincidence economic indicators is found to be 1.31% and that of the two stage estimation method is around 1.44%. If we take consideration into the fact that it measures the business cycle in one month earlier, we come to the conclusion that the two stage estimation is of great use.

• Proceedings of the KIPE Conference
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• 2003.11a
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• pp.113-118
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• 2003

The proposed rotor time constant estimation method can be applied to the direct vector control system of induction motor with flux observer In this paper the flux observer proposed by Gopinath model are used. This paper presents a new scheme for on-line estimation of rotor time constant using estimated rotor flux phase and current model rotor flux phase. The major advantage of this method are its dynamic correction capability, simplicity and accuracy as well as independence from change in motor parameter. simulation results are presented which demonstrate the effectiveness of the on line rotor time constant estimation.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.152-152
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• 2019

This study demonstrates a novel approach of remotely sensed estimates of stream flow at fifteen hydrological station in the Han River Basin, Korea. Multi-temporal data of the European Space Agency's Sentinel-1 SAR satellite from 19 January, 2015 to 25 August, 2018 is used to develop and validate the flow estimation model for each station. The flow estimation model is based on a power law relationship established between the remotely sensed surface area of water at a selected reach of the stream and the observed discharge. The satellite images were pre-processed for thermal noise, radiometric, speckle and terrain correction. The difference in SAR image brightness caused by the differences in SAR satellite look angle and atmospheric condition are corrected using the histogram matching technique. Selective area filtering is applied to identify the extent of the selected stream reach where the change in water surface area is highly sensitive to the change in stream discharge. Following this, an iterative procedure called the Optimum Threshold Classification Algorithm (OTC) is applied to the multi-temporal selective areas to extract a series of water surface areas. It is observed that the extracted water surface area and the stream discharge are related by the power law equation. A strong correlation coefficient ranging from 0.68 to 0.98 (mean=0.89) was observed for thirteen hydrological stations, while at two stations the relationship was highly affected by the hydraulic structures such as dam. It is further identified that the availability of remotely sensed data for a range of discharge conditions and the geometric properties of the selected stream reach such as the stream width and side slope influence the accuracy of the flow estimation model.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.7
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• pp.423-427
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• 2005

In this paper, we proposed a new ARMA model identification which estimate the parameters to make the current prediction error uncorrelated with the past one. As good properties of the proposed method, we show the uniqueness, consistency of the estimate and asymptotic normality of the estimation error. Via simulation results, we show that the proposed method give good estimates for various systems which have different power spectrum. Moreover, the estimation of gyroscope random errors shows that the proposed method is applicable to the real data.

• Proceedings of the KIPE Conference
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• 2017.07a
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• pp.126-127
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• 2017

An accurate State Of Charge (SOC) estimation of battery is the most important technique for Electric Vehicles (EVs) and Energy Storage Systems (ESSs). In this paper a new integrated Unscented Kalman Filter-Particle Filter (UKF-PF) is employed to estimate the SOC of a $LiFePO_4$ battery cell and a significant improvement is obtained as compared to the other methods. The parameters of the battery is modeled by the second order Auto Regressive eXogenous (ARX) model and estimated by using Recursive Least Square (RLS) method to calculate value of each element in the model. The proposed algorithm is established by combining a parameter identification technique using RLS method with ARX model and an SOC estimation technique using UKF-PF.