• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.105-110
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• 2009

Distributed Generation (DG) is predicted to play a important role in electric power system in the near future. insertion of DG system into existing distribution network has great impact on real-time system operation and planning. It is widely accepted that micro turbine generation (MTG) systems are currently attracting lot of attention to meet customers need in the distributed power generation market In order to investigate the performance of MT generation systems, their efficient modeling is required. This paper presents the modeling and simulation of a MT generation system suitable for grid-connected operation. The system comprises of a permanent magnet synchronous generator driven by a MT. A brief description of the overall system is given, and mathematical models for the MT and permanent magnet synchronous generator are presented. Also, the use of Power electronics in conditioning the power output of the generating system is demonstrated. Simulation studies with MATLAB/Simulink have been carried out in grid-connected operation mode of a DG system. The control strategies for grid connected operation mode of DG system is also presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2562-2570
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• 2014

One of the important hints for inferring the function of unknown proteins is the knowledge about protein subcellular localization. Recently, there are considerable researches on the prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular localization. In this paper, label power-set classification is improved for the accurate prediction of multiple subcellular localization. The predicted multi-labels from the label power-set classifier are combined with their prediction probability to give the final result. To find the accurate probability estimates of multi-classes, this paper employs pair-wise comparison and error-correcting output codes frameworks. Prediction experiments on protein subcellular localization show significant performance improvement.

• New & Renewable Energy
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• v.5 no.4
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• pp.39-43
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• 2009

The variation of inflow at stream and hydrologic performance for small scale hydro power(SSHP) plants due to climate change have been studied. The model, which can predict flow duration characteristic of stream, was developed to analyze the variation of inflow caused from rainfall condition. And another model to predict hydrologic performance for SSHP plants is established. Monthly inflow data measured at Andong dam for 32 years were analyzed. The existing SSHP plant located in upstream of Andong dam was selected and analyzed hydrologic performance characteristics. The predicted results from the developed models show that the data were in good agreement with measured results of long term inflow at Andong dam and the existing SSHP plant. Inflow and ideal hydro power potential had increased greatly in recent years, however, these did not lead annual energy production increment of existing SSHP plant. As a results, it was found that the models represented in this study can be used to predict the primary design specifications and inflow of SSHP plants effectively.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.517-520
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• 2004

The power flow analysis(PFA) can be effectively used to predict structural vibration in medium-to-high frequency ranges. In this paper, vibration experiment has been performed to observe the analytical characteristics of the power flow analysis of the vibration of a plate. In the experiment, the loss factor of the plate and the input mobility at a source point have been measured. The data for the loss factor has been used as the input data to predict the vibration of the plate with PFA. The frequency response functions have been measured over the surface of the plate. The comparison between the experimental results and the predicted results for the frequency response functions showed that PFA can be an effective tool to predict structural vibration in medium-to-high frequency ranges.

• Nuclear Engineering and Technology
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• v.40 no.1
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• pp.77-86
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• 2008

This paper describes the nonlinear analyses of a 1:4 scale model of a prestressed concrete containment vessel (PCCV) using an axisymmetric model and a three-dimensional model. These two models are refined by comparison of the analysis results and with testing results. This paper is especially focused on the analysis of behavior under pressure and the temperature effects revealed using an axisymmetric model. The temperature-dependent degradation properties of concrete and steel are considered. Both geometric and material nonlinearities, including thermal effects, are also addressed in the analyses. The Menetrey and Willam (1995) concrete constitutive model with non-associated flow potential is adopted for this study. This study includes the results of the predicted thermal and mechanical behaviors of the PCCV subject to high temperature loading and internal pressure at the same time. To find the effect of high temperature accident conditions on the ultimate capacity of the liner plate, reinforcement, prestressing tendon and concrete, two kinds of analyses are performed: one for pressure only and the other for pressure with temperature. The results from the test on pressurization, analysis for pressure only, and analyses considering pressure with temperatures are compared with one another. The analysis results show that the temperature directly affects the behavior of the liner plate, but has little impact on the ultimate pressure capacity of the PCCV.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1448-1456
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• 2022

This paper studies a model to diagnose efficiency reduction of inverter using Multilayer Perceptron(MLP). In this study, two inverter data which started operation at different day was used. A Multilayer Perceptron model was made to predict photovoltaic power data of the latest inverter. As a result of the model's performance test, the Mean Absolute Percentage Error(MAPE) was 4.1034. The verified model was applied to one-year-old and two-year-old data after old inverter starting operation. The predictive power of one-year-old inverter was larger than the observed power by 724.9243 on average. And two-year-old inverter's predictive value was larger than the observed power by 836.4616 on average. The prediction error of two-year-old inverter rose 111.5572 on a year. This error is 0.4% of the total capacity. It was proved that the error is meaningful difference by t-test. The error is predicted value minus actual value. Which means that PV system actually generated less than prediction. Therefore, increasing error is decreasing conversion efficiency of inverter. Finally, conversion efficiency of the inverter decreased by 0.4% over a year using this model.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.19 no.1
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• pp.27-35
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• 2023

Pressure tubes are the main components of PHWR core and serve as the pressure boundary of the primary heat transport system. However, because pressure tubes have changed their geometrical dimensions under the severe operating conditions of high temperature, high pressure and neutron irradiation according to the increase of operation time, all dimensional changes should be predicted to ensure that dimensions remain within the allowable design ranges during the operation. Among the deformations, the diameter expansion due to creep leads to the increase of bypass flow which may not contribute to the fuel cooling, the decrease of critical channel power and finally the deration of the power to maintain the operational safety margin. This study is focused on the modeling of the expansion of the pressure tube diameter based on the operating conditions and measured diameter data. The pressure tube diameter expansion was modeled using the neutron flux and temperature distributions of each fuel channel and each fuel bundle as well as the measured diameter data. Although the basic concept of the current modeling approach is simple, the diameter prediction results using the developed methodology showed very good agreement with the real data, compared to the existing methodology.

• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.846-854
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• 2024

FEMAXI-ATF is being developed for fuel performance modeling of SiC cladded UO2 fuel with focuses on modeling pellet-cladding mechanical interactions (PCMI). The code considers probability distributions of mechanical strengths of monolithic SiC (mSiC) and SiC fiber reinforced SiC matrix composite (SiC/SiC), while it models pseudo-ductility of SiC/SiC and propagation of cladding failures across the wall thickness direction in deterministic manner without explicitly modeling cracks based on finite element method in one-dimensional geometry. Some hypothetical BWR power ramp conditions were used to test sensitivities of different model parameters on the analyzed PCMI behavior. The results showed that propagation of the cladding failure could be modeled by appropriately reducing modulus of elasticities of the failed wall element, so that the mechanical load of the failed element could be re-distributed to other intact elements. The probability threshold for determination of the wall element failure did not have large influence on the predicted power at failure when the threshold was varied between 25 % and 75 %. The current study is still limited with respect to mechanistic modeling of SiC failure as it only models the propagation of the cladding wall element failure across the homogeneous continuum wall without considering generations and propagations of cracks.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.383-388
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• 2024

This paper is a study to derive the predicted value of power generation based on the photovoltaic power generation data measured in Jeollanam-do, South Korea. Multivariate variables such as direct current, alternating current, and environmental data were measured in the inverter to measure the amount of power generation, and pre-processing was performed to ensure the stability and reliability of the measured values. Correlation analysis used only data with high correlation with power generation in time series data for prediction using partial autocorrelation function (PACF). Deep learning models were used to measure the amount of power generation to predict the amount of photovoltaic power generation, and the results of correlation analysis of each multivariate variable were used to increase the prediction accuracy. Learning using refined data was more stable than when existing data were used as it was, and the solar power generation prediction algorithm was improved by using only highly correlated variables among multivariate variables by reflecting the correlation analysis results.