• JOURNAL OF ELECTRICAL WORLD
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• no.4 s.100
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• pp.12-15
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• 1985

• JOURNAL OF ELECTRICAL WORLD
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• no.5 s.77
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• pp.9-13
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• 1983

• Asian Journal of Atmospheric Environment
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• v.11 no.4
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• pp.270-282
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• 2017

To evaluate the deposition amount on a ground surface, mesoscale numerical models coupled with atmospheric chemistry are widely used for larger horizontal domains ranging from a few to several hundreds of kilometers; however, these models are rarely applied to high-resolution simulations. In this study, the performance of a dry and wet deposition model is investigated to estimate the amount of deposition via computational fluid dynamics (CFD) models with high grid resolution. Reynolds-averaged Navier-Stokes (RANS) simulations are implemented for a cone and a two-dimensional ridge to estimate the dry deposition rate, and a constant deposition velocity is used to obtain the dry deposition flux. The results show that the dry deposition rate of RANS generally corresponds to that observed in wind-tunnel experiments. For the wet deposition model, the transport equation of a new scalar concentration scavenged by rain droplets is developed and used instead of the scalar concentration scavenged by raindrops falling to the ground surface just below the scavenging point, which is normally used in mesoscale numerical models. A sensitivity analysis of the proposed wet deposition procedure is implemented. The result indicates the applicability of RANS for high-resolution grids considering the effect of terrains on the wet deposition.

• Journal of the Korean Ceramic Society
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• v.45 no.12
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• pp.760-765
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• 2008

In this study, the effects of small amounts (${\leq}3\;mol%$) of Li doping on the sintering characteristics and electrochemical performance of $(ZrO_2)_{0.89}(ScO_{1.5})_{0.1}(CeO_2)_{0.01}$ (ScSZ) were investigated. By adding 3 mol% lithium, the densification temperature of ScSZ was reduced from the conventional temperature of $1400^{\circ}C$ to $1200^{\circ}C$. It was found that Li doping also led to changes in the Zr:Sc ratio at the grain boundaries. Correspondingly, the dispersion of lithium zirconia at the grain boundaries accelerated the growth of ScSZ grains and increased the grain boundary resistance at temperatures below $450^{\circ}C$. At elevated temperatures of $450{\sim}750^{\circ}C$, the electrical conductivity of the ScSZ after doping remained almost unchanged under air and reducing atmospheres. These results suggest that the addition of lithium is promising for use in low temperature co-firing of ScSZ-based components for intermediate temperature solid oxide fuel cells.

• Proceedings of the Korean System Dynamics Society
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• 2006.04a
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• pp.101-122
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• 2006

A series of system dynamics model was developed for forecasting demand and supply of human resource in the electricity industry. To forecast demand of human resource in the electric power industry, BLS (Bureau of Labor Statistics) methodology was used. To forecast supply of human resource in the electric power industry, forecasting on the population of our country and the number of students in the department of electrical engineering were performed. After performing computer simulation with developed system dynamics model, it is discovered that the shortage of human resource in the electric power industry will be 3,000 persons per year from 2006 to 2015, and more than a double of current budget is required to overcome this shortage of human resource.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.171-172
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• 2007

The need for open architecture based IP network is becoming increasingly critical because the electric power industry has begun to upgrade to digital systems. In this paper, we implemented IPv6 testbed and experimented IPv6 performance actually for examination on IPv6 applications to electric power communication network. We achieved preliminary tests relevant to IPv6 to solve expected problems before.

• Proceedings of the SAREK Conference
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• 2004.06a
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• pp.172-172
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• 2004

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.395-403
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• 2017

An efficient evaluation method for the probability of a tornado missile strike without using the Monte Carlo method is proposed in this paper. A major part of the proposed probability evaluation is based on numerical results computed using an in-house code, Tornado-borne missile analysis code, which enables us to evaluate the liftoff and flight behaviors of unconstrained objects on the ground driven by a tornado. Using the Tornado-borne missile analysis code, we can obtain a stochastic correlation between local wind speed and flight distance of each object, and this stochastic correlation is used to evaluate the conditional strike probability, $Q_V(r)$, of a missile located at position r, where the local wind speed is V. In contrast, the annual exceedance probability of local wind speed, which can be computed using a tornado hazard analysis code, is used to derive the probability density function, p(V). Then, we finally obtain the annual probability of tornado missile strike on a structure with the convolutional integration of product of $Q_V(r)$ and p(V) over V. The evaluation method is applied to a simple problem to qualitatively confirm the validity, and to quantitatively verify the results for two extreme cases in which an object is located just in the vicinity of or far away from the structure.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.399-404
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• 2020

There are many attempts to apply AI technology to diagnose facilities or improve the work efficiency of the power industry. The emergence of new machine learning technologies, such as deep learning, is accelerating the digital transformation of the power sector. The problem is that traditional power systems face security risks when adopting state-of-the-art AI systems. This adoption has convergence characteristics and reveals new cybersecurity threats and vulnerabilities to the power system. This paper deals with the security measures and implementations of the power system using machine learning. Through building a commercial facility operations forecasting system using machine learning technology utilizing power big data, this paper identifies and addresses security vulnerabilities that must compensated to protect customer information and power system safety. Furthermore, it provides security guidelines by generalizing security measures to be considered when applying AI.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1387-1390
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• 2005

Recently, foreign utilities emphasize the importance of the value-added services based on Information Technology(IT) as one of the strategic technologies for establishing a new power system in the future digital society. They develop many different types of the value-added services and apply the systems for customer. In domestic case, the data from the Automatic Meter Reading (AMR) System is used only for calculating the tariffs. Data from the AMR system can be strategic assets for utilities to provide the value-added services for customer. Development of the value-added services for utilities and customer needs processing and managing the AMR data. In this paper, the energy consulting service, which is adequate to new power system environment, is suggested for development of the value-added services. The application of the suggested service will bring the effect of reducing the monthly bill for customer. Also the service will give not only the effective demand side management(DSM) and load control, but also reduction of the investment for utilities.