• Nuclear Engineering and Technology
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• v.36 no.3
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• pp.248-262
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• 2004

Turbulence models are separately assessed for a three dimensional thermal-hydraulic analysis of the integral reactor SMART. Seven models (mixing length, k-l, standard $k-{\epsilon},\;k-{\epsilon}-f{\mu},\;k-{\epsilon}-v2$, RRSM, and ERRSM) are investigated for flat plate channel flow, rotating channel flow, and square sectioned U-bend duct flow. The results of these models are compared to the DNS data and experiment data. The results are assessed in terms of many aspects such as economical efficiency, accuracy, theorization, and applicability. The standard $k-{\epsilon}$ model (high Reynolds model), the $k-{\epsilon}-v2$ model, and the ERRSM (low Reynolds models) are selected from the assessment results. The standard $k-{\epsilon}$ model using small grid numbers predicts the channel flow with higher accuracy in comparison with the other eddy viscosity models in the logarithmic layer. The elliptic-relaxation type models, $k-{\epsilon}-v2$, and ERRSM have the advantage of application to complex geometries and show good prediction for near wall flows.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.75-80
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• 2012

In this paper, a new energy harvesting technology using stray electric field of an electric power line is presented. It is found that energy can be harvested and stored in the storage capacitor that is connected to a cylindrical aluminum foil wrapped around a commercial insulated 220 V power line. The average current flowing into 47 ${\mu}F$ storage capacitor is about 4.53 ${\mu}A$ with 60 cm long cylindrical aluminum foil, and it is possible to operate wireless sensor node to transmit RF data every 42 seconds. The harvested average power is about 47 ${\mu}W$ in this case. Since the energy can be harvested without removing insulating sheath, it is believed that the proposed harvesting technology can be applied to power the sensor nodes in wireless ubiquitous sensor network and smart grid system.

• Nuclear Engineering and Technology
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• v.45 no.2
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• pp.219-222
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• 2013

Alloy 690 has been selected as a steam generator tubing material for SMART owing to a near immunity to primary water stress corrosion cracking. The steam generators of SMART are faced with a neutron flux due to the integrated arrangement inside a reactor vessel, and thus it is important to know the irradiation effects of the thermal conductivity of Alloy 690. Alloy 690 was irradiated at HANARO to fluences of (0.7-28) ${\times}10^{19}n/cm^2$ (E>0.1MeV) at $250^{\circ}C$, and its thermal conductivity was measured using the laser-flash equipment in the IMEF. The thermal conductivity of Alloy 690 was dependent on temperature, and it was a good fit to the Smith-Palmer equation, which modified the Wiedemann-Franz law. The irradiation at $250^{\circ}C$ did not degrade the thermal conductivity of Alloy 690, and even showed a small increase (1%) at fluences of (0.7~28) ${\times}10^{19}n/cm^2$ (E>0.1MeV).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.1-7
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• 2019

Due to the highly concentrated power plants integrated through the limited transmission lines in Korea, a Special Protection System(SPS) has been applied to stabilize the power systems by instantly tripping the pre-determined generators in a large-scaled power plant when a fault occurs on the drawing transmission lines. Moreover, power outputs of those generators are constrained to avoid any activation of Under Frequency Load Shedding(UFLS) even after those generators are tripped by SPS action. For this, this paper proposes a method for calculating the required capacity of Energy Storage System(ESS) expected to relieve the operating constraints to generators using its fast response for controlling power system frequency. The proposed method uses the generator acceleration energy to derive the stable condition during the SPS action. In addition, its effectiveness is verified by the case studies adopting actual SPS operations in Korean power systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.1957-1980
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• 2021

The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.47-56
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• 2023

Hydrogen energy is an eco-friendly energy that produces heat and electricity with high energy efficiency and does not emit harmful substances such as greenhouse gases and fine dust. In particular, smart hydrogen energy is an economical, sustainable, and safe future smart hydrogen energy service, which means a service that stably operates based on 'data' by digitally integrating hydrogen energy infrastructure. In this paper, in order to implement a data-based hydrogen charging station demand forecasting model, three hydrogen charging stations (Chuncheon, Sokcho, Pyeongchang) installed in Gangwon-do were selected, supply and demand data of hydrogen charging stations were secured, and 7 machine learning and deep learning algorithms were used. was selected to learn a model with a total of 27 types of input data (weather data + demand for hydrogen charging stations), and the model was evaluated with root mean square error (RMSE). Through this, this paper proposes a machine learning-based hydrogen charging station energy demand prediction model for optimal hydrogen energy supply and demand.

• Journal of Digital Convergence
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• v.9 no.5
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• pp.1-9
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• 2011

The smart grid can create benefits such as the expansion of consumer choice and flexibility enhancement, adaption to future electric power industry change and the increasing use of renewable energy sources. Consumers can make a contribution to improve the overall effectiveness of system through active receptive response. They can enhance the energy consumption efficiency based on more information from service providers. The Smart Grid Promotion Act, which was enacted in April 2011, contains consumer protection provisions such as information collecting, sharing, and protection measures. On this reason, it is needed to expand promotion and education regarding the smart grid to improve the consumer awareness, and the schemes to enhance smart grid consumer acceptance should be established.

• Journal of Satellite, Information and Communications
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• v.6 no.2
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• pp.131-135
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• 2011

In this paper, we describe the design and implementation of wireless smart plug having AC power sensor and intelligent standby power control algorithm for energy sensor network. The adaptive standby power control algorithm has function to apply different threshold of standby power by using learning algorithm depending on electric equipments. As using the proposed algorithm, user convenience will be more better and power consumption can be more reduced. The implemented prototypes of wireless smart plug and wireless access point were tested to verify the required functions and performance. As a result, we confirmed practicality of wireless smart power sensor and satisfaction of given design specifications.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.495-501
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• 2017

The introduction of smart grid, which is an innovative application of digital processing and communications to the power grid, might lead to more and more cyber threats originated from IT systems. In other words, The Energy Management System (EMS) and other communication networks interact with the power system on a real time basis, so it is important to understand the interaction between two layers to protect the power system from potential cyber threats. This paper aims to identify and clarify the cyber security risks and their interaction with the power system in Smart Grid. In this study, the optimal power flow (OPF) and Power Flow Tracing are used to assess the interaction between the EMS and the power system. Through OPF and Power Flow Tracing based analysis, the physical and economic impacts from potential cyber threats are assessed, and thereby the quantitative risks are measured in a monetary unit.

• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.44-48
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• 2015

The smart grid is a next-generation power grid to create a new value-added information technology. Power providers and consumers exchange information in real-time bi-directional, and optimize energy efficiency with using the smart grid. This paper describes the concept of demand response of the communication system used in the protocol, implementation of demand response systems with demand response scenarios for power reduction through the air conditioning control.