• Journal of Power Electronics
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• v.19 no.5
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• pp.1289-1302
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• 2019

In this paper, an optimal allocation method of a hybrid active power filter in an active distribution network is designed based on the differential evolution algorithm to resolve the harmonic generation problem when a distributed generation system is connected to the grid. A distributed generation system model in the calculation of power flow is established. An improved back/forward sweep algorithm and a decoupling algorithm are proposed for fundamental power flow and harmonic power flow. On this basis, a multi-objective optimization allocation model of the location and capacity of a hybrid filter in an active distribution network is built, and an optimal allocation scheme of the hybrid active power filter based on the differential evolution algorithm is proposed. To verify the effect of the harmonic suppression of the designed scheme, simulation analysis in an IEEE-33 nodes model and an experimental analysis on a test platform of a microgrid are adopted.

• Korean Journal of Remote Sensing
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• v.27 no.1
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• pp.51-58
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• 2011

Accurate DEM(Digital Elevation Model) generation using satellite images is an active research topic. This paper focuses on generation of a DEM with multiple Kompsat-2 images. For DEM generation, we applied an orbit-attitude sensor model and a RPM sensor model to stereo and multiple Kompsat-2 images respectively. For matching, we used an object-space based matching method. Through the result of this experiment, we could confirm that the sensor model from multiple images is more accurate than the model from stereo images. Also DEM from multiple images gave much better performance than DEM from stereo images.

• Journal of Radiation Protection and Research
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• v.41 no.3
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• pp.237-244
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• 2016

Background: Korea Atomic Energy Research Institute is developing a fission product transport module for predicting the behavior of radioactive materials in the primary cooling system of a nuclear power plant as a separate module, which will be connected to a severe accident analysis code, Core Meltdown Progression Accident Simulation Software (COMPASS). Materials and Methods: This fission product transport (COMPASS-FP) module consists of a fission product release model, an aerosol generation model, and an aerosol transport model. In the fission product release model there are three submodels based on empirical correlations, and they are used to simulate the fission product gases release from the reactor core. In the aerosol generation model, the mass conservation law and Raoult's law are applied to the mixture of vapors and droplets of the fission products in a specified control volume to find the generation of the aerosol droplet. In the aerosol transport model, empirical correlations available from the open literature are used to simulate the aerosol removal processes owing to the gravitational settling, inertia impaction, diffusiophoresis, and thermophoresis. Results and Discussion: The COMPASS-FP module was validated against Aerosol Behavior Code Validation and Evaluation (ABCOVE-5) test performed by Hanford Engineering Development Laboratory for comparing the prediction and test data. The comparison results assuming a non-spherical aerosol shape for the suspended aerosol mass concentration showed a good agreement with an error range of about ${\pm}6%$. Conclusion: It was found that the COMPASS-FP module produced the reasonable results of the fission product gases release, the aerosol generation, and the gravitational settling in the aerosol removal processes for ABCOVE-5. However, more validation for other aerosol removal models needs to be performed.

• Animal Bioscience
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• v.34 no.4
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• pp.471-481
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• 2021

Objective: A comprehensive study was conducted to study the effects of partition of variance on accuracy of genetic parameters and genetic trends of economic traits in Vanaraja male line/project directorate-1 (PD-1) chicken. Methods: Variance component analysis utilizing restricted maximum likelihood animal model was carried out with five generations data to delineate the population status, direct additive, maternal genetic, permanent environmental effects, besides genetic trends and performance of economic traits in PD-1 chickens. Genetic trend was estimated by regression of the estimated average breeding values (BV) on generations. Results: The body weight (BW) and shank length (SL) varied significantly (p≤0.01) among the generations, hatches and sexes. The least squares mean of SL at six weeks, the primary trait was 77.44±0.05 mm. All the production traits, viz., BWs, age at sexual maturity, egg production (EP) and egg weight were significantly influenced by generation. Model four with additive, maternal permanent environmental and residual effects was the best model for juvenile growth traits, except for zero-day BW. The heritability estimates for BW and SL at six weeks (SL6) were 0.20±0.03 and 0.17±0.03, respectively. The BV of SL6 in the population increased linearly from 0.03 to 3.62 mm due to selection. Genetic trend was significant (p≤0.05) for SL6, BW6, and production traits. The average genetic gain of EP40 for each generation was significant (p≤0.05) with an average increase of 0.38 eggs per generation. The average inbreeding coefficient was 0.02 in PD-1 line. Conclusion: The population was in ideal condition with negligible inbreeding and the selection was quite effective with significant genetic gains in each generation for primary trait of selection. The animal model minimized the over-estimation of genetic parameters and improved the accuracy of the BV, thus enabling the breeder to select the suitable breeding strategy for genetic improvement.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.55-61
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• 2022

In order to reduce greenhouse gases, the main culprit of global warming, the United Nations signed the Climate Change Convention in 1992. Korea is also pursuing a policy to expand the supply of renewable energy to reduce greenhouse gas emissions. The expansion of renewable energy development using solar power led to the expansion of wind power and solar power generation. The expansion of renewable energy development, which is greatly affected by weather conditions, is creating difficulties in managing the supply and demand of the power system. To solve this problem, the power brokerage market was introduced. Therefore, in order to participate in the power brokerage market, it is necessary to predict the amount of power generation. In this paper, the prediction system was used to analyze the Yonchuk solar power plant. As a result of applying solar insolation from on-site (Model 1) and the Korea Meteorological Administration (Model 2), it was confirmed that accuracy of Model 2 was 3% higher. As a result of comparative analysis of the DNN and RNN models, it was confirmed that the prediction accuracy of the DNN model improved by 1.72%.

• Journal of Korean Society of Transportation
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• v.25 no.6
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• pp.161-170
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• 2007

The purpose of this study is to find the solution that overcomes the existing assumption of symmetric cost functions in multi-class assignment. In the assignment problem, the assumption of a symmetric cost function means that the link cost is determined by each unique mode and is not affected by any other modes. In this study, the authors have applied a diagonalized algorithm and a heuristic model based on column generation to a multi-class assignment model and analyzed the result. Through the study, the authors found that the diagonalized algorithm produces equilibrium solutions by the initial convergence condition. In contrast to the diagonalized algorithm, the column generation algorithm has improved the solution model to overcome the problem of equilibrium solutions in the diagonalized algorithm.

• Journal of Korean Society for Quality Management
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• v.44 no.4
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• pp.713-733
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• 2016

Purpose: The primary objective of this case study is to establish a COQ(Cost of Quality) management system for power generation industries. Key topics of this study include collecting COQ elements, their classifications, COQ computation formula, and determining COQ improvement projects. Results: A comprehensive set of COQ elements have been isolated for electric power generation companies. The COQ elements were classified in such a way that they could be managed according to the PAF model as well as the SIPOC diagram. This study showed that a systematic approach could be established for monitoring the COQ elements and using them in the process of improving quality competitiveness. Methods: The PAF(Prevention-Appraisal-Failure) model has been employed in the process of collecting COQ elements for a power generation company. All the cost of quality elements were first examined through an extensive review of articles and books in the field of quality. The cost elements were then refined and augmented by conducting a comparative study with international standards. The COQ elements have been verified by a group of quality managers and classified according to both the PAF model and the SIPOC diagram for better understanding in the entire organization. An improvement strategy has been also proposed by using a typical COQ level of power generation companies. Conclusion: The conventional PAF model was used in establishing a COQ management system for power generation industries. This case study illustrates the procedure about identification, classification and computation of quality costs, including selection of improvement projects. The system can be used not only for observing the current state of cost elements related to quality, but also for planning an improvement strategy using the ratio of cost classification.

• Journal of Navigation and Port Research
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• v.29 no.5 s.101
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• pp.431-437
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• 2005

In korea, Freight generation models developed in korea were estimated by spatial unit method which predict freight flow by traffic zone. But it is difficult to predict freight generation using these models, because there are the difference of the totality method of sampling data on freight volume and the variability of the variables by these models on each case study, This study developed new estimation model to predict freight flow which is generated from each company using the characteristics of each company such as the freight outbound & inbound volume, the number of employee, sales, gross area, land area. This model is simpler than the that of spatial unit and can apply to the other region. The subjects of study were companies in metropolitan area and types of model were exponential regression models. The adequate explanatory variable in the models were sales. this study have a uniqueness apply micro research method to estimate freight generation not use spatial unit method but use flow unit method by each company unit.

• Plant Journal
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• v.16 no.4
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• pp.26-32
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• 2020

The performance monitoring system in the power plant should have the capability to estimate power generation efficiency accurately. Several power generation efficiency models have been proposed for the combined heat and power (CHP) plant which produces both electricity and process steam(or heating energy, hereinafter expressed by process steam only). However, most of the models are not sufficiently accurate due to the wrong evaluation of the process steam value. The study suggests Electricity Conversion Efficiency (ECE) model with determination of the heat rate of process steam using operational data. The suggested method is applied to the design data and the resulted trajectory curve of power generation efficiency meets the data closely with R2 99.91%. This result confirms that ECE model with determination of the model coefficient using the operational data estimate the efficiency so accurately that can be used for performance monitoring of CHP plant.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.825-832
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• 2022

In this paper, we discuss the design and implementation of predictive and diagnostic models for realizing intelligent predictive models by collecting and storing the power output of agricultural photovoltaic power generation systems. Our model predicts the amount of photovoltaic power generation using RNN, LSTM, and GRU models, which are recurrent neural network techniques specialized for time series data, and compares and analyzes each model with different hyperparameters, and evaluates the performance. As a result, the MSE and RMSE indicators of all three models were very close to 0, and the R2 indicator showed performance close to 1. Through this, it can be seen that the proposed prediction model is a suitable model for predicting the amount of photovoltaic power generation, and using this prediction, it was shown that it can be utilized as an intelligent and efficient O&M function in an agricultural photovoltaic system.