• Journal of Ocean Engineering and Technology
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• v.38 no.3
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• pp.103-114
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• 2024

Remote sensing wave observation data are crucial when analyzing ocean waves, the main external force of coastal disasters. Nevertheless, it has limitations in accuracy when used in low-wind environments. Therefore, this study collected the raw data from MIROS Wave and Current Radar (MWR) and wave radar at the Ieodo Ocean Research Station (IORS) and applied the optimal filter by combining filters provided by MIROS software. The data were validated by a comparison with South Jeju ocean buoy data. The results showed it maintained accuracy for significant wave height, but errors were observed in significant wave periods and extreme waves. Hence, this study used an artificial neural network (ANN) to improve these errors. The ANN was generalized by separating the data into training and test datasets through stratified sampling, and the optimal model structure was derived by adjusting the hyperparameters. The application of ANN effectively improved the accuracy in significant wave periods and high wave conditions. Consequently, this study reproduced past wave data by enhancing the reliability of the MWR, contributing to understanding wave generation and propagation in storm conditions, and improving the accuracy of wave prediction. On the other hand, errors persisted under high wave conditions because of wave shadow effects, necessitating more data collection and future research.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.419-424
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• 2012

This paper discusses the state-of-the-art techniques in real-time state estimation for the Smart Microgrids. The most popular method used in traditional power system state estimation is a Weighted Least Square(WLS) algorithm which is based on Maximum Likelihood(ML) estimation under the assumption of static system state being a set of deterministic variables. In this paper, we present a survey of dynamic state estimation techniques for Smart Microgrids based on Belief Propagation (BP) when the system state is a set of stochastic variables. The measurements are often too sparse to fulfill the system observability in the distribution network of microgrids. The BP algorithm calculates posterior distributions of the state variables for real-time sparse measurements. Smart Microgrids are modeled as a factor graph suitable for characterizing the linear correlations among the state variables. The state estimator performs the BP algorithm on the factor graph based the stochastic model. The factor graph model can integrate new models for solar and wind correlation. It provides the Smart Microgrids with a way of integrating the distributed renewable energy generation. Our study on Smart Microgrid state estimation can be extended to the estimation of unbalanced three phase distribution systems as well as the optimal placement of smart meters.

• Tunnel and Underground Space
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• v.20 no.6
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• pp.446-454
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• 2010

CAES-G/T (Compressed Air Energy Storage - Gas Turbine) power generation is a likely option for the buffer facility stabilizing the fluctuation of the renewable powers, such as wind and solar powers. Considering the geological conditions, the underground CAES facility is most probable if the CAES-G/T generation is planed in Korea. In this kind of facility, a concrete plug is installed to seal the compressed air in the container, so that the selection of the shape and dimension of concrete plug could be a critical design factor. The stability evaluation of two types of plug was carried out by investigating the distribution of the factor of safety in the plugs and the distribution of contact pressure over the contact surface. The analysis result shows that the taper-shaped plug is more structurally stable than the wedge-shaped plug for the given geological condition. Possible separation of the rock-concrete interface around the spring line of the wedge-shaped plug is anticipated, which means the possible leakage of compressed air through the side wall and also means the poor mobilization of frictional resistance on that area.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.71-78
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• 2020

In the case of power generation using renewable energy, power production may not be smooth due to the influence of the weather. The energy storage system (ESS) is used to increase the efficiency of solar and wind power generation. ESS has been continuously fired due to a lack of battery protection systems, operation management, and control system, or careless installation, leading to very big casualties and economic losses. ESS stability and battery protection system operation management technology is indispensable. In this paper, we present a battery level calculation algorithm and a failure prediction algorithm for ESS optimization and stable operation. The proposed algorithm calculates the correct battery level by accumulating the current amount in real-time when the battery is charged and discharged, and calculates the battery failure by using the voltage imbalance between battery cells. The proposed algorithms can predict the exact battery level and failure required to operate the ESS optimally. Therefore, accurate status information on ESS battery can be measured and reliably monitored to prevent large accidents.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.9
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• pp.436-443
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• 2017

Recently, the development of new energy technologies has become a hot topic due to problems,such as global warming. Unlike renewable energy technologies, such as solar energy generation, solar power, and wind power, which are optimized to achieve medium or above output power, the output power of energy harvesting technology is very small and has not received much attention. On the other hand, as the mobile industry has been revitalized recently, the utility of energy harvesting technology has been reevaluated. In addition, the technology of tracking the maximum power point has been actively researched. This paper proposes a new MPPT(Maximum Power Point Tracking) control method for a TEM(thermoelectric module) for load resistance. The V-I curve characteristics and internal resistance of TEM were analyzed and the conventional MPPT control methods were compared. The P&O(Perturbation and Observation) control method is more accurate, but it is less economical than the CV (Constant Voltage)control method because it usestwo sensors to measure the voltage and current source. The CV control method is superior to the P&O control method in economic aspects because it uses only one voltage sensor but the MPP is not matched precisely. In this paper, a method wasdesigned to track the MPP of TEM combining the advantages of the two control method. The proposed MPPT control method wasverified by PSIM simulation and H/W implementation.

• Clean Technology
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• v.27 no.4
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• pp.277-290
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• 2021

A supercapacitor, also called an ultracapacitor or an electrochemical capacitor, stores electrochemical energy by the adsorption/desorption of electrolytic ions or a fast and reversible redox reaction at the electrode surface, which is distinct from the chemical reaction of a battery. A supercapacitor features high specific power, high capacitance, almost infinite cyclability (~ 100,000 cycle), short charging time, good stability, low maintenance cost, and fast frequency response. Supercapacitors have been used in electronic devices to meet the requirements of rapid charging/discharging, such as for memory back-up, and uninterruptible power supply (UPS). Also, their use is being extended to transportation and large industry applications that require high power/energy density, such as for electric vehicles and power quality systems of smart grids. In power generation using intermittent power sources such as solar and wind, a supercapacitor is configured in the energy storage system together with a battery to compensate for the relatively slow charging/discharging time of the battery, to contribute to extending the lifecycle of the battery, and to improve the system power quality. This article provides a concise overview of the principles, mechanisms, and classification of energy storage of supercapacitors in accordance with the electrode materials. Also, it provides a review of the status of recent research and patent, product, and market trends in supercapacitor technology. There are many challenges to be solved to meet industrial demands such as for high voltage module technologies, high efficiency charging, safety, performance improvement, and competitive prices.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.415.2-415.2
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• 2016

Triboelectric nanogenerator (TENG) is one of ways to convert mechanical energy sound, waves, wind, vibrations, and human motions to available electrical energy. The principal mechanism to generate electrical energy is based on contact electrification on material surface and electrostatic induction between electrodes. The performance of TENG are dependent on amount of the input mechanical energy and characteristics of triboelectric materials. Furthermore, the whole TENG system including mechanical structure and electrical system can effect on output performance of TENG. In this work, we investigated the effect of gear train on output performance and power conversion efficiency (PCE) of TENG under a given input energy. We applied the gear train on mechanical structure to improve the contact rate. We measured the output energy under a constant input energy by controlling the size of the working gear. We prepared gears with gear ratios (rin/rw) of 1, 1.7, and 5. Under the constant input energy, the voltage and current from our gear-based TENG system were enhanced up to the maximum of 3.6 times and 4.4 times, respectively. Also, the PCE was increased up to 7 times at input frequency of 1.5 Hz. In order to understand the effect of kinematic design on TENG system, we performed a capacitor experiment with rectification circuit that provide DC voltage and current. Under the input frequency of 4.5 Hz, we obtained a 3 times enhanced rectifying voltage at a gear ratio of 5. The measured capacitor voltage was enhanced up to about 8 fold in using our TENG system. It is attributed that our gear-based TENG system could improve simultaneously the magnitude as well as the generation time of output power, finally enhancing output energy. Therefore, our gear-based TENG system provided an effective way to enhance the PCE of TENGs operating at a given input energy.

• Korean Journal of Construction Engineering and Management
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• v.18 no.2
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• pp.91-98
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• 2017

Due to climate change, countries around the world are actively investing in renewable energy, reducing fossil fuel use. 23.7% of world electricity is supplied by renewable energy. As the technology continues to develop, it is in a level to compete in terms of power generation cost, and investment conditions are improving. However, investment in renewable energy projects is not easy. This study analyzed trends of domestic and international researches on economics assessment applying real options analysis to investment decisions of hydro, solar, and wind power projects, which account for a large portion of renewable energy. This study provides (1) the difference between the traditional economic method and the real options analysis, (2) the application process, and (3) the uncertainty elements and option type of the renewable energy project presented by many studies. The real options analysis is suitable for the detailed investment strategy by considering the uncertainties of the renewable energy project and applying the option to improve the profit or to avoid the risk.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.61.2-61.2
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• 2015

In the past decades, green energy, such as solar energy, wind power, hydropower, biomass energy, geothermal energy, and so on, has been widely investigated and developed to solve energy shortage. Recently, organic solar cells have attracted much attention, because they have many advantages, including low-cost, flexibility, light weight, and easy fabrication [1-3]. Organic solar cells are as a potential candidate of the next generation solar cells. In this abstract, to improve the power conversion efficiency and the stability, the inverted polymer solar cells with various structures were developed [4-6]. The novel cell structures included the P3HT:PCBM inverted polymer solar cells with AZO nanorods array, with pentacene-doped active layer, and with extra P3HT interfacial layer and PCBM interfacial layer. These three difference structures could respectively improve the performance of the P3HT:PCBM inverted polymer solar cells. For the inverted polymer solar cells with AZO nanorods array as the electronic transportation layer, by using the nanorod structure, the improvement of carrier collection and carrier extraction capabilities could be expected due to an increase in contact area between the nanorod array and the active layer. For the inverted polymer solar cells with pentacene-doped active layer, the hole-electron mobility in the active layer could be balanced by doping pentacene contents. The active layer with the balanced hole-electron mobility could reduce the carrier recombination in the active layers to enhance the photocurrent of the resulting inverted polymer solar cells. For the inverted polymer solar cells with extra P3HT and PCBM interfacial layers, the extra PCBM and P3HT interfacial layers could respectively improve the electron transport and hole transport. The extra PCBM interfacial layer served another function was that led more P3HT moving to the top side of the absorption layer, which reduced the non-continuous pathways of P3HT. It indicated that the recombination centers could be further reduced in the absorption layer. The extra P3HT interfacial layer could let the hole be more easily transported to the MoO3 hole transport layer. The high performance of the novel P3HT:PCBM inverted polymer solar cells with various structures were obtained.

• Asian Journal of Atmospheric Environment
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• v.8 no.1
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• pp.25-34
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• 2014

In winter 2013, extreme air pollution by fine particulate matter ($PM_{2.5}$) in China attracted much public attention. In order to simulate the $PM_{2.5}$ pollution, the Community Multiscale Air Quality model driven by the Weather Research and Forecasting model was applied to East Asia in a period from 1 January 2013 to 5 February 2013. The model generally reproduced $PM_{2.5}$ concentration in China with emission data in the year 2006. Therefore, the extreme $PM_{2.5}$ pollution seems to be mainly attributed to meteorological (weak wind and stable) conditions rather than emission increases in the past several years. The model well simulated temporal and spatial variations in $PM_{2.5}$ concentrations in Japan as well as China, indicating that the model well captured characteristics of the $PM_{2.5}$ pollutions in both areas on the windward and leeward sides in East Asia in the study period. In addition, contribution rates of four anthropogenic emission sectors (power generation, industrial, residential and transportation) in China to $PM_{2.5}$ concentration were estimated by conducting zero-out emission sensitivity runs. Among the four sectors, the residential sector had the highest contribution to $PM_{2.5}$ concentration. Therefore, the extreme $PM_{2.5}$ pollution may be also attributed to large emissions from combustion for heating in cold regions in China.