• 한국정보디스플레이학회:학술대회논문집
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• 2007.08a
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• pp.609-612
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• 2007

This paper describes a new method to predict the load current of a dc-dc converter. The load current is calculated using the video information of the PDP. The output capacitance of the dc-dc converter can be reduced by utilizing the predicted load current, which results in a cost reduction of the power system in the PDP.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.339-342
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• 2000

Repetitive Load Prediction is proposed for the UPS inverter application of the second order deadbeat controller which is robust against the calculation time delay and the parameter variation and which gets fast response against the load variation. The proposed technique predicts the load current ahead of two sampling time using that the load current is periodic. This is effective under nonlinear load condition. The proposed technique is derived theoretically and verified through simulation and experimental result.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.327-332
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• 2009

This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.39-49
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• 2013

This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.

• Journal of the Society of Naval Architects of Korea
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• v.46 no.6
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• pp.603-610
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• 2009

Ice load is one of the important design parameters for the construction of icebreaking vessels. In this paper, the design ice load prediction for the icebreaking vessels under normal operating condition in ice-covered sea is discussed. The ice loads under normal operating condition are expected from sea trials in moderate ice conditions. In this sense the extreme ice loads during heavy ramming or accidental collision are not considered. Current study describes the global ice load on the hull of the icebreaking vessels. Available ice load data from full-scale sea trials are collected and analyzed according to various ship-ice interaction parameters including displacement, stem angle, speed of a ship and flexural strength and thickness of sea ice. The ice load prediction formula is compared with the collected full-scale sea trials data and it shows a good agreement.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.382-389
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• 2002

Recently, auger-drilled piling has been widely used in urban area to reduce the air pollution and noise. But this construction method that its basic theory was introduced from Japan may be changed depending on the each piling company and construction field condition. Therefore, the design code and management method for auger-drilled piling is not defined yet. Especially, the lack of research on the bearing capacity of auger-drilled piling leads to the absence of rational bearing capacity prediction equation. This paper presents the optimum design code and economical construction method of the auger-drilled piling by proposing the new bearing capacity prediction equation based on the site specific soil types and construction conditions. In this paper, existing bearing capacity prediction equations and current pile load tests were compared. And the end bearing capacity and skin friction characteristics were also analyzed by comparing the results of CAPWAP. From the results of analysis, a reliable bearing capacity prediction equation considered soil types is proposed.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.49 no.4
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• pp.246-255
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• 2000

This paper deals with a novel full digital control method of the three-phase PWM inverter for UPS. The voltage and current of output filter capacitor as state variables are the feedback control input. In addition, a double deadbeat control consisting of a d-q current minor loop and a d-q voltage major loop, both with precise decoupling, have been developed. The switching pulse width modulation based on SVM is adopted so that the capacitor current should be exactly equal to its reference current. In order to compensate the calculation time delay, the predictive control is achieved by the current·voltage observer. The load prediction is used to compensate the load disturbance by disturbance observer with deadbeat response. The experimental results show that the proposed system offers an output voltage with THD less than 2% at a full nonlinear load.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.369-382
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(quality of service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.

• Smart Structures and Systems
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• v.29 no.6
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• pp.799-805
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• 2022

Permanent magnet synchronous motors (PMSMs) are widely used in systems requiring high control precision, efficiency, and reliability. Predicting the remaining useful life (RUL) with health monitoring of PMSMs prevents catastrophic failure and ensures reliable operation of system. In this study, a model-based method for predicting the RUL of PMSMs using phase current and vibration signals is proposed. The proposed method includes feature selection and RUL prediction based on a particle filter with a degradation model. The Paris-Erdogan model describing micro fatigue crack propagation is used as the degradation model. An experimental set-up to conduct accelerated life test, capable of monitoring various signals was designed in this study. Phase current and vibration data obtained from an accelerated life test of the PMSMs were used to verify the proposed approach. Features extracted from the data were clustered based on monotonicity and correlation clustering, respectively. The results identify the effectiveness of using the current data in predicting the RUL of PMSMs.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.6
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• pp.65-72
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• 2001

This paper deals with a novel full digital control of the single-phase PWM(Pulse Width Modulation) inviter for UPS(Uninterruptible Power Supp1y). The voltage and current of output filter capacitor as a state variable are the feedback control input. In the proposed scheme a double deadbeat control consisting of minor current control loop and major voltage control loop have been developed In addition, a second order deadbeat currents control which should be exactly equal to its reference in two sampling time without error and overshoot is proposed to remove the influence of the calculation time delay. The load current prediction is achieved to compensate the load disturbance. The simulation and experimental result shows that the proposed system offers an output voltage with THD(Total Harmonic Distortion) less than 5% at a full nonlinear load.