• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.21-29
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• 2014

In general, a simulation project requires much time and money since we should develop a model that works similarly to the system at a level consistent with the project purposes. Therefore, more active research studies are required to reduce the time needed for the modeling process. This is achievable by minimizing the possible trial and error during the model development process through the appropriate conceptual model design and the automated generation of the simulation model. This paper presents a tool automatically generating an Arena model after developing a conceptual simulation model. Because our proposed tool is based on the popular Microsoft Excel and Visio, it is expected to be practically used at many industrial sites. Finally, we showed the effectiveness of the newly suggested tool by applying it to an imaginary simulation project.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.5
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• pp.85-91
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• 2006

Co-generation systems have been spreading rapidly over the past 10 years in Korea and most of these systems are interconnected with electric power systems. However, better control and protection models are still needed for analysis of these systems to ensure stable operation with the grid. This paper proposes improved EMTDC models for control fad protection analysis of grid-connected co-generation systems. Through on-site characteristic testing, the models were developed and the model parameters were determined. The models were applied to a field co-generation system, and analysis of control and protection was performed showing a good match to the simulation results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.10
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• pp.1423-1427
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• 2014

Quantity of the solar power generation is heavily influenced by weather. In other words, due to difference in insolation, different quantity may be generated. However, it does not mean all areas with identical insolation produces same quantity because of various environmental aspects. Additionally, geographic factors such as altitude, height of plant may have an impact on the quantity. Hence, through this research, we designed a system to predict efficiency of the solar power generation system by applying insolation, weather factor such as duration of sunshine, cloudiness parameter and location. By applying insolation, weather data that are collected from various places, we established a system that fits with our nation. Apart from, we produced a geographic model equation through utilizing generated data installed nationwide. To design a prediction model that integrates two factors, we apply fuzzy algorithm, and validate the performance of system by establishing simulation system.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.461-470
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• 2014

This paper presents a probabilistic power flow (PPF) analysis method for distribution network incorporating the randomness and correlation of photovoltaic (PV) generation. Based on the multivariate kernel density estimation theory, the probabilistic model of PV generation is proposed without any assumption of theoretical parametric distribution, which can accurately capture not only the randomness but also the correlation of PV resources at adjacent locations. The PPF method is developed by combining the proposed PV model and Monte Carlo technique to evaluate the influence of the randomness and correlation of PV generation on the performance of distribution networks. The historical power output data of three neighboring PV generators in Oregon, USA, and 34-bus/69-bus radial distribution networks are used to demonstrate the correctness, effectiveness, and application of the proposed PV model and PPF method.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.69-76
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• 2008

In this thesis, we implemented the prototype system for the class code generator based on consistent code generation process and standard type, the class to be component unit. Particularly, we proposed relationship rule to solve the difficult problem by the object-oriented language to association and aggregation between classes based on component, through this method we can make to consistent code generation standard. Also it is adopted to component model construction which is generated code using code generation, and it can be basic assembly and deployment of business components to reusable target in developing application system.

• Nuclear Engineering and Technology
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• v.53 no.5
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• pp.1658-1663
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• 2021

Research on climate change and global warming on the power generation systems are rapidly increasing because of the Importance of the sustainable energy supply, thus the electricity supply since its growing share, in the end, uses energy supply. However, some researchers conducted this field, but many research gaps are not mentioned and filled in this field's literature since the lack of general statements and the quantitative models and formulation of the issue. In this research, an exergy-based model is implemented to model a set of six power generation technologies (combined cycle, gas turbine, nuclear plant, solar PV, and wind turbine) and use this model to simulate each technology's responses to climate change impacts. Finally, using these responses to define and calculate a formulation for the relationship between the system's energy performance in different environmental situations and a dimensionless index to quantize each power technology's reliability against the climate change impacts called the Pahlev reliability index (P-index) of the power technology. The results have shown that solar and nuclear technologies are the most, and wind turbines are the least reliable power generation technologies.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.319-328
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• 2021

In this paper, we perform prediction of amount of electric power plant for complex of wind plant using multi-layer perceptron in order to calculate exact calculation of capacity of ESS to maximize profit through generation and to minimize generation cost of wind generation. We acquire wind speed, direction of wind and air density as variables to predict the amount of generation of wind power. Then, we merge and normalize there variables. To train model, we divide merged variables into data as train and test data with ratio of 70% versus 30%. Then we train model by using training data, and we alsouate the prediction performance of model by using test data. Finally, we present the result of prediction in amount of wind power.

• Environmental and Resource Economics Review
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• v.22 no.4
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• pp.691-726
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• 2013

We develop a hybrid model which allows the change in electricity generation mix by adding the electricity-sector components of bottom-up model to the conventional CGE model. The electricity sector is represented as a sum of separate generation technologies, each of which has the form of DRTS (Decreasing Returns to Scale) production function, unlike the conventional CGE model. We compare the effects of the 30% emission reduction target using the hybrid model with those using the conventional CGE model. The cost of meeting the target is lower with the hybrid model than the conventional CGE. It is consistent with previous studies in that adding the bottom-up components to the top-down model reduces the cost of emission reduction. In an extra analysis we find that an additional regulation like RPS (Renewable Portfolio Standard) increases the cost.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.222-227
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• 2022

In this paper, we implemented a Korean text generation and classification model based on a deep learning algorithm that can be applied to various industries. It consists of two implemented GAN-based Korean handwriting generation models and CNN-based Korean handwriting classification models. The GAN model consists of a generator model for generating fake Korean handwriting data and a discriminator model for discriminating fake handwritten data. In the case of the CNN model, the model was trained using the 'PHD08' dataset, and the learning result was 92.45. It was confirmed that Korean handwriting was classified with % accuracy. As a result of evaluating the performance of the classification model by integrating the Korean cursive data generated through the implemented GAN model and the training dataset of the existing CNN model, it was confirmed that the classification performance was 96.86%, which was superior to the existing classification performance.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2228-2239
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• 2015

A novel integrated optimization method based on the Generalized Bender’s Decomposition (GBD) is proposed to combine both generation and transmission expansion problems. Most of existing researches on the integrated expansion planning based on the GBD theory incorporate DC power flow model to guarantee the convergence and improve the computation time. Inherently the GBD algorithm based on DC power flow model cannot consider variables and constraints related bus voltages and reactive power. In this paper, an integrated optimization method using the GBD algorithm based on a linearized AC power flow model is proposed to resolve aforementioned drawback. The proposed method has been successfully applied to Garver’s six-bus system and the IEEE 30-bus system which are frequently used power systems for transmission expansion planning studies.