• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.423-431
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• 2022

The UPS(Uninterruptible Power Supply) system operates in the battery charging mode when the grid is normal, and in the UPS mode, which is the battery discharge mode when a grid error occurs. Since the UPS must supply the same voltage as the grid to the load within 4 [ms] in case of a grid error, the switching time and power recovery time should be short when controlling the output voltage and current of the UPS, and the power failure detection time is also important. The power outage detection algorithm using DFT(Discrete Fourier Transform) proposed in this paper compares the grid voltage waveform with the voltage waveform including the 9th harmonic generated through DFT using Schmitt trigger to detect power outage faster than the existing power outage monitoring algorithm. There are advantages. Therefore, it is possible to supply instant and stable power when switching modes in the UPS system. The multi-power-applied UPS system proposed in this paper uses DFT, which is faster than the conventional blackout monitoring algorithm in detecting power failure, to provide stable power to the load in a shorter time than the existing power outage monitoring algorithm when a system error occurs. The detection method was applied. The changeover time of mode switching was set to less than 4 [ms], which is 1/4 of the system cycle, in accordance with KSC 4310 regulation, which was established by the Industrial Standards Council on the regulation of uninterruptible power supply. A 10 [kW] UPS system in which commercial voltage, vehicle generator, and auxiliary diesel generator can be connected to each of the proposed transfer devices was constructed and the feasibility was verified by conducting an experiment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1074-1077
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• 1993

The objective of this paper is to develop a fuzzy logic based decision-making system to detect low current faults using multiple detection algorithms. This fuzzy system utilizes a fuzzy expert model which executes an operation without complicated mathematical models. This fuzzy system decides the performance weights of the detection algorithms. The weights and the turnouts of the detection algorithms discriminate faults from normal events. This system can also be a generic group decision-making tool for other areas of power system protection.

• Proceedings of the KIPE Conference
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• 2011.07a
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• pp.378-379
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• 2011

PV system's islanding occurs when the uitilty grid is removed but local sources continue to operate and provide power to local loads. Islanding is one of the serious problems in an electric power system connected with dispersed power sources. Also, this can present safety hazards and the possibility of damage to other electric equipments. If the real and reactive power of RLC load and PV system are closely matched, islanding detection by passive methods becomes difficult. Novel islanding detection method is proposed for grid-connected PV system.

• Proceedings of the KIPE Conference
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• 2004.11a
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• pp.133-137
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• 2004

PV system's islanding occurs when the uitilty grid is removed but local sources continue to operate and provide power to local loads. Islanding is one of the serious problems m an electric power system connected with dispersed power sources. Also, this can present safety hazards and the possibility of damage to other electric equipments. If the real and reactive power of RLC load and PV system are closely matched, islanding detection by passive methods becomes difficult. Novel islanding detection method using frequency drift is proposed for grid-connected PV system.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.5
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• pp.217-225
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• 2005

In a power system, an out-of-step condition causes a variety of risk such as serious damage to system elements, tripping of loads and generators, mal-operation of relays, etc. Therefore, it is very important to detect the out-of-step condition and take a proper measure. Several out-of-step detection methods have been employed in relays until now. Most common method used for an out-of-step detection is based on the transition time through the blocking impedance area in R-X diagram. Also, the R-R dot out-of-step relay, the out-of-step prediction method and the adaptive out-of-step relay using the equal area criterion (EAC) and Global Positioning Satellite (GPS) technology have been developed. This paper presents the out-of-step detection algorithm using the time variation of the complex power. The complex power is calculated and the mechanical power of the generator is estimated by using the electrical power, and then the out-of-step detection algorithm which is based on the complex power and the estimated mechanical power, is presented. This algorithm may detect the instant when the generator angle passes the Unstable Equilibrium Point (UEP). The proposed out-of-step algorithm is verified and tested by using Alternative Transient Program/Electromagnetic Transient Program (ATP/EMTP) MODELS.

• ETRI Journal
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• v.45 no.4
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• pp.650-665
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• 2023

A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.

• Journal of the Korea Safety Management & Science
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• v.22 no.1
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• pp.1-8
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• 2020

Maintenance of power distribution facilities is a significant subject in the power supplies. Fault caused by deterioration in power distribution facilities may damage the entire power distribution system. However, current methods of diagnosing power distribution facilities have been manually diagnosed by the human inspector, resulting in continuous pole accidents. In order to improve the existing diagnostic methods, a thermal image analysis model is proposed in this work. Using a thermal image technique in diagnosis field is emerging in the various engineering field due to its non-contact, safe, and highly reliable energy detection technology. Deep learning object detection algorithms are trained with thermal images of a power distribution facility in order to automatically analyze its irregular energy status, hereby efficiently preventing fault of the system. The detected object is diagnosed through a thermal intensity area analysis. The proposed model in this work resulted 82% of accuracy of detecting an actual distribution system by analyzing more than 16,000 images of its thermal images.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.236-237
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• 2006

A microgrid is a new type of power system which is formed by the interconnection of small generator to distribution systems. Microgrids are able to supply electric power to their loads independently, which is called with islanding operation, even if they're separated from the power system. As a result, microgrids must be equipped with specific islanding detection schemes for the islanding operation of microgrids. This paper studies an islanding detection method considering reactive power. The proposed method is dealing with the reactive power of the cable which connects between a microgrid and a power system. To show the validity of the proposed method, many islanding operation cases are tested by varying the load conditions of microgrids.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.4
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• pp.152-159
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• 2007

In this paper, a new design technique on the LRF which is useful for low power laser and a CBDA(Cummulative Binary Detection Algorithm) is proposed. The LD(Laser Diode) and Si-APD(Silicon Avalanche Photo Diode) are used for saving a power. In order to prove the detection range, the Si-APD binary data are accumulated before the range computation and the range finding algorithm. A prototype of the proposed DLRF(Diode Laser Range Finder) system was made and tested. An experimental result shows that the DLRF system have the same detection range using a less power(almost 1/32) than an usual military LRF. The proposed DLRF can be applied to the Unmanned Vehicles, Robot and Future Combat System of a tiny size and a low power LRF.

• Corrosion Science and Technology
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• v.10 no.4
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• pp.151-155
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• 2011

The iron oxide particles could be resulted from the corrosion of the circulating water system of a power plant. Because it may be one of the trouble materials which affect the power generation efficiency due to the deposition on steam generator tube and turbine blade, the continuous observation of its concentration is very important. The laser induced break-down detection (LIBD) technology was applied to monitor continuously the concentration of corrosion products with the detection limit of ppb level. The measurement system consists of a Nd:YAG pulsed laser, a polarizing beam splitter, a flow-type sample cell, an acoustic emission sensor, a high speed data acquisition board, a personal computer, etc.. The performance test results confirmed that this technology can be effective to monitor the corrosion product concentration of the circulating water system of a power plant.