• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.325-327
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• 2000

This paper addresses the issues of the external system modeling and power system state estimation with the external model. A set of significant branches is identified in the external system based on their branch outage sensitivities on the tie-lines. Measurements on these branches are transffered to internal system and updated in real-time. The state estimator is run for different loading conditions by using the actual measurements for the internal system and selected significant external system measurements while keeping the rest of the external system measurements at their base case values. Simulation results are presented using the IEEE 118 bus system as an example.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.4
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• pp.411-419
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• 2005

In this paper, a sliding mode observer for solar array current estimation in the photovoltaic power generation system is presented. The solar array current estimation Information is obtained from the sliding mode observer and fed into the maximum power point tracker to update the reference voltage. The parameter values such as inverter dc link capacitances cm be changed up to 50$\%$ from their nominal values and the linear observer can't estimate the correct state values under the parameter variations and noisy environments. The configuration of sliding mode observer is simple, but it shows the robust tracking performance against parameter variations and modeling uncertainties. In this paper, the method for constructing the sliding mode observer using equivalent control input is presented and the convergence of the proposed observer is verified by the Lyapunov method. The mathematical modeling and the experimental results verify the validity of the proposed method.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.114-117
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• 2001

In the Electric Vehicle(EV) driving system, the Battery Management System(BMS) is very important and an essential equipment. Particularly, BMS monitors the State of Charge(SOC), voltage, current, and temperature of the battery modules when Electric Vehicle is in the state of motoring or charging. Major roles of BMS are like these the first, estimation of State of Charge(SOC), the second, detection of the unbalance of the voltage between battery modules, the third, control of the available limit of the voltage and temperature of batteries by monitoring the batteries status during motoring or charging. In this research, We have focused on estimating SOC of battery according to the status of Electric Vehicle and the BMS operation algorithm. The result for algorithm of SOC estimation is presented. It have been modified, compensated, and verified by means of the experiment.

• Smart Structures and Systems
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• v.14 no.2
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• pp.247-266
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• 2014

Conventional cantilevered piezoelectric energy harvesters (PEHs) are usually fabricated with continuous electrode configuration (CEC), which suffers from the electrical cancellation at higher vibration modes. Though previous research pointed out that the segmented electrode configuration (SEC) can address this issue, a comprehensive evaluation of the PEH with SEC has yet been reported. With the consideration of delivering power to a common load, the AC outputs from all segmented electrode pairs should be rectified to DC outputs separately. In such case, theoretical formulation for power estimation becomes challenging. This paper proposes a method based on equivalent circuit model (ECM) and circuit simulation to evaluate the performance of the PEH with SEC. First, the parameters of the multi-mode ECM are identified from theoretical analysis. The ECM is then established in SPICE software and validated by the theoretical model and finite element method (FEM) with resistive loads. Subsequently, the optimal performances with SEC and CEC are compared considering the practical DC interface circuit. A comprehensive evaluation of the advantageous performance with SEC is provided for the first time. The results demonstrate the feasibility of using SEC as a simple and effective means to improve the performance of a cantilevered PEH at a higher mode.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3703-3716
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• 2022

Target alignment technology is one of the most critical technologies in laser fusion experiments and is an important technology related to the success of laser fusion experiments. In this study, by combining the open-loop and closed-loop errors of the target alignment, the Kalman state observer is used to estimate the position of the target, which improves the observation precision of the target alignment. Then the optimized result is used to guide the alignment of the target. This method can greatly optimize the target alignment error and reduce uncertainty. With the improvement of the target alignment precision, it will greatly improve the reliability and repeatability of the experiments' results, thereby improving the success rate of the experiments.

• Journal of IKEEE
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• v.24 no.1
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• pp.333-338
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• 2020

This paper proposes a method of estimating the SOC(State of Charge) of a battery cell using a neural network algorithm. To this, we implement a battery SOC estimation simulator and derive input and output data for neural network learning through charge and discharge experiments at various temperatures. Finally, the performance of the battery SOC estimation is analyzed by comparing with the experimental value by Ah-counting using Matlab/Simulink program and confirmed that the error rate can be reduced to less than 3%.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.270-272
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• 1999

As the power system automation, especially the substation automation is currently a major issue, it has been widely studied. Since a measured value usually has an error that results in the communication process and the characteristics of a measurement device mainly depending on the reliability. The accuracy and reliability of measured values at a substation is an important factor for substation automation. In this paper, a state estimation algorithm is proposed for domestic 154kV/22.9kV distribution substations, and the results showed promising application.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.401-402
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• 2014

State of Charge (SOC) and State of Health (SOH) are the key issues for the application of Absorbent Glass Mat (AGM) type battery in Idle Start Stop (ISS) system which is popularly integrated in Electric Vehicles (EVs). However, battery parameters strongly depend on SOC, current rate and temperature and significantly change over the battery life cycles. In this research, a novel method for SOC, SOH estimation which combines the Auto Regressive with external input (ARX) method using for online parameters prediction and Dual Extended Kalman Filter (DEKF) algorithm considering hysteresis is proposed. The validity of the proposed algorithm is verified by the simulation and experiments.

• The KIPS Transactions:PartA
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• v.19A no.3
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• pp.139-146
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• 2012

In the server cluster environment, one of the ways saving energy is to control server's power according to traffic conditions. This is to determine the ON/OFF state of servers according to energy usage of data center and each server. To do this, we need a way to estimate each server's energy. In this paper, we use a software-based power consumption estimation model because it is more efficient than the hardware model using power meter in terms of energy and cost. The traditional software-based power consumption estimation model has a drawback in that it doesn't know well the computing status of servers because it uses only the idle status field of CPU. Therefore it doesn't estimate consumption power effectively. In this paper, we present a CPU field based power consumption estimation model to estimate more accurate than the two traditional models (CPU/Disk/Memory utilization based power consumption estimation model and CPU idle utilization based power consumption estimation model) by using the various status fields of CPU to get the CPU status of servers and the overall status of system. We performed experiments using 2 PCs and compared the power consumption estimated by the power consumption model (software) with that measured by the power meter (hardware). The experimental results show that the traditional model has about 8-15% average error rate but our proposed model has about 2% average error rate.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.544-548
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• 1999

Using Observer, on the sensorless vector control system is a novel techniques for modern induction motor control. In this paper, a speed estimation algorithm of an induction motor using an extended kalman filter was proposed. Extended kalman filter can solve the problem, that have steady state error of estimated speed in flux and slip estimation method. The extended Kalman filter is employed to identify the speed of an induction motor and rotor flux based on the measured quantities such as stator current and DC link voltage. In order to confirming above proposal, computer simulation carried out using Matlab Simulink and show the effectiveness of the control drives for induction motor speed estimation.