• New & Renewable Energy
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• v.2 no.3
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• pp.15-22
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• 2006

As the distributed generation becomes more reliable and economically feasible, it is expected that a higher application of the distributed generation units would be interconnected to the existing grids. This new market penetration using the distributed generation technology is linked to a large number of factors like economics and performance, safety and reliability, market regulations, environmental issues, or grid connection standards. KEPCO, a government company in Korea, has performed the project to identify and evaluate the performance of Micro Gas Turbine(MGT) technologies focused on 30, 60kW-class grid-connected optimization and combined Heat & Power performance. This paper describes the results for the mechanical, electrical, and environmental tests of MGT on actual grid-connection under Korean regulations. As one of the achievements, the simulation model of Exhaust-gas Absorption Chiller was developed, so that it will be able to analyze or propose new distributed generation system using MGT. In addition, KEPCO carried out the field testing of the MGT Cogeneration system at the R&D Center Building, KEPCO. The field test was conducted in order to respond to a wide variety of needs for heat recovery and utilization. The suggested method and experience for the evaluation of the distributed generation will be used for the introduction of other distributed generation technologies into the grid in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.1
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• pp.179-184
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• 2004

This paper presents an image segmentation algorithm to obtain the 3D body shape data that the grid pattern and the body contour lute in the background image are extracted using the new proposed hybrid method. The body contour line is extracted based on maximum biased anisotropic recognition(MaxBAR) algorithm which recognizes the most strong and robust edges in the image since the normal derivative at the edges is large, while the tangential derivatives can be small. The grid patterns within body contour lines are extracted by grid pattern detection (GPD). The body contour lilies and the grid patterns are combined. The consecutive run test based on heuristic method is used to link the disconnected line and reduce noise line. This proposed segmentation method is more effective than the conventional method which uses a gradient and a laplacian operator, verified with application two conventional method.

• Journal of IKEEE
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• v.24 no.3
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• pp.877-882
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• 2020

This paper presents performance comparison study of current control methods for grid connected inverter (GCI) system. Different current control methods have been developed for GCI systems and each controller has its own advantages and limitations. Steady state and transient dynamic performance of the GCI current controllers are compared in this paper. The performance of the proposed command feedforward control (CFFC) and disturbance rejection control (DRC) is analyzed before and after application to all GCI current controllers. The proposed CFFC and DRC control algorithms is analyzed in a frequency domain and the simulation and experiment models of each GCI current control methods are developed for verification of the performance.

• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.2
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• pp.93-98
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• 2012

A precise measurement of the grid voltage is one of the essential techniques, which is required to connect a renewable energy to the grid. In general, when a filter is used to eliminate unnecessary harmonics and noises, a signal is distorted by phase delay, amplitude attenuation, and other distortions. And the response characteristic of a controller is directly affected by bandwidth of cut-off frequency of the filter. To alleviate this problems, we propose an effective algorithm based on DFT(Discrete Fourier Transform) instead of approaching the filter application. The proposed algorithm ensures high precise measurement of the grid voltage because it can extract the fundamental and harmonics from the raw signal without any distortions. The high performance of the proposed algorithm is verified by PSIM simulation and experiments of Grid-Connected VSI.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2003.09a
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• pp.189-201
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• 2003

Grid is a new Information Technology (IT) concept of "super Internet" for high-performance computing: worldwide collections of high-end resources - such as supercomputers, storage, advanced instruments and immerse environments. The Grid is expected to bring together geographically and organizationally dispersed computational resources, such as CPUs, storage systems, communication systems, real-time data sources and instruments, and human collaborators. The term "the Grid" was coined in the mid l990s to denote a proposed distributed computing infrastructure for advanced science and engineering. The term computational Grids refers to infrastructures aimed at allowing users to access and/or aggregate potentially large numbers of powerful and sophisticated resources. More formally, Grids are defined as infrastructure allowing flexible, secure, and coordinated resource sharing among dynamic collections of individuals, institutions and resources referred to as virtual Organizations. GRID is an emerging IT as a kind of next generation Internet technology which will fit very well with Agrometeorological services in the future. I believe that it would contribute to the resource sharing in AgroMeteorology by providing super computing power, virtual storage, and efficient data exchanges, especially for developing countries that are suffering from the lack of resources for their agmet services at national level. Thus, the establishment of CAgM-GRID based on existing RAMINSII is proposed as a part of FWIS of WMO.part of FWIS of WMO.

• 한국지구물리탐사학회:학술대회논문집
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• 2008.10a
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• pp.57-62
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• 2008

Finite difference method using not general SSG(standard staggered grid) but RSG(rotated staggered grid) was applied to simulation of elastic wave propagation. Special free surface boundary condition such as imaging method is needed in finite difference method using SSG in elastic wave propagation but free surface boundary condition in finite difference method using RSG is easily solved with adding air layer. Recently PML(Perfectly Matched layer) is widely used to eliminate artificial reflection waves from finite boundary because of its' greate efficiency. Absorbing ability of CPML(convolutional Perfectly Matched Layer) that is more efficient than that of PML was applied to FDM using RSG in this study. The results of CPML eliminated artificial boundary waves very effectively in FDM using RSG in being compared with that of Cerjan's absorbing method.

• Journal of Satellite, Information and Communications
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• v.7 no.2
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• pp.1-7
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• 2012

In recent years, importance of environmental protection and energy resources has increased. Therefore, eco - friendly consumption patterns is rapidly increased. Accordingly, efficient energy consumption technology is noted in a variety of industries. Smart grid technology can improve energy efficiency by using IT technology. Among them, the demand response is an critical factor in the smart grid. In this paper, we present basic concept of smart grid and demand response. In addition, we present demand response technology and its application examples.

• Journal of Power Electronics
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• v.15 no.4
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• pp.1006-1017
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• 2015

This paper presents a finite control set model predictive control (FCS-MPC) strategy for the AC/DC matrix converter used in grid-connected battery energy storage system (BESS). First, to control the grid current properly, the DC current is also included in the cost function because of input and output direct coupling. The DC current reference is generated based on the dynamic relationship of the two currents, so the grid current gains improved transient state performance. Furthermore, the steady state error is reduced by adding a closed-loop. Second, a Luenberger observer is adopted to detect the AC input voltage instead of sensors, so the cost is reduced and the reliability can be enhanced. Third, a switching state pre-selection method that only needs to evaluate half of the active switching states is presented, with the advantages of shorter calculation time, no high dv/dt at the DC terminal, and less switching loss. The robustness under grid voltage distortion and parameter sensibility are discussed as well. Simulation and experimental results confirm the good performance of the proposed scheme for battery charging and discharging control.

• Journal of Hydrogen and New Energy
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• v.33 no.5
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• pp.591-597
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• 2022

Recently, technologies for efficient power grid operation have become important due to climate change. For this reason, predicting power demand using deep learning is being considered, and it is necessary to understand the influence of characteristics of each region, industrial structure, and climate. This study analyzed the power demand of New Jersey in US, with a high urbanization rate and a large service industry, and West Virginia in US, a low urbanization rate and a large coal, energy, and chemical industries. Using recurrent neural network algorithm, the power demand from January 2020 to August 2022 was learned, and the daily and weekly power demand was predicted. In addition, the power grid operation based on the power demand forecast was discussed. Unlike previous studies that have focused on the deep learning algorithm itself, this study analyzes the regional power demand characteristics and deep learning algorithm application, and power grid operation strategy.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.