• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.2
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• pp.155-163
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• 2013

As the increasing HIF(High Impedance Fault) with the arc cannot be easily detected for the low fault current magnitude compared to actual load in distribution line. However, the arcing current shows that the magnitude varies with time and the signal is asymmetric. In addition, discontinuous changes occur at starting point of arc. Considering these characteristics, wavelet transformation of actual current data shows difference between before and after the fault. Althogh raw data(detail coefficient) of wavelet transform may not be directly applied to HIF detection logic in a device, there are several developing methods of HIF monitoring data using the original wavelet coefficients. In this paper, a simple and effective developing methods of HIF monitoring data were analized by using the signal data through an actual HIF experiment to apply them to economic devices. The methods using the sumation of the wavelet coefficient squares in one cycle of the fundamental frequency as the energies of the wavelet coefficeits and the sumation of the absolute values were compared. Besides, the improved method which less occupies H/W resouces and can be applied to field detection devices was proposed. and also Verification of this HIF detection method through field test on distribution system in KEPCO power testing center was performed.

• Journal of the Korean Society of Safety
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• v.29 no.5
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• pp.47-53
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• 2014

Despite the automatic fire alarm system, according to the national fire data system of national emergency management agency, the fires account for 40,932 incidents, 2,184 injuries and about 430 billion won in property losses in 2013. Since the conventional automatic fire alarm system has several weaknesses related to electrical signal such as noise, surge, lighting, etc. Most fires are mainly caused by electrical faults, mechanical problem, chemical, carelessness and natural. The electrical faults such as line to ground fault, line to line fault, electrical leakage and arc are one of the major problems in fire. This paper describes the development of a novel real-time fire monitoring system algorithm including fault detection function which puts the existing optic smoke and heat detectors for fire detection with current and voltage sensors in order to utility fault monitoring using high accuracy DAQ measurement system with LabVIEW program. The fire detection and electrical fault monitoring with a proposed a new detection algorithm are implemented under several test. The fire detection and monitoring system operates according to the proposed algorithm well.

• Smart Structures and Systems
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• v.15 no.5
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• pp.1233-1252
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• 2015

This study investigates the problem of crack detection in post-buckled beam-type structures. The beam under the axial compressive force has a crack, assumed to be open and through the width. The crack, which is modeled by a massless rotational spring, divides the beam into two segments. The crack detection is considered as an optimization problem, and the weighted sum of the squared errors between the measured and computed natural frequencies is minimized by the bees algorithm. To find the natural frequencies, the governing nonlinear equations of motion for the post-buckled state are first derived. The solution of the nonlinear differential equations of the two segments consists of static and dynamic parts. The differential quadrature method along with an arc length strategy is used to solve the static part, while the same method is utilized for the solution of the linearized dynamic part and the extraction of the natural frequencies of the cracked beam. The investigation includes several numerical as well as experimental case studies on the post-buckled simply supported and clamped-clamped beams having open cracks. The results show that several parameters such as the amount of applied compressive force and boundary conditions influences the outcome of the crack detection scheme. The identification results also show that the crack position and depth can be predicted well by the presented method.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.255-263
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• 2022

3D object detection pipelines often incorporate RGB-based object detection methods such as YOLO, which detects the object classes and bounding boxes from the RGB image. However, in complex environments where objects are heavily cluttered, bounding box approaches may show degraded performance due to the overlapping bounding boxes. Mask based methods such as Mask R-CNN can handle such situation better thanks to their detailed object masks, but they require much longer time for data preparation compared to bounding box-based approaches. In this paper, we present a 3D object recognition pipeline which uses either the YOLO or Mask R-CNN real-time object detection algorithm, K-nearest clustering algorithm, mask reduction algorithm and finally Principal Component Analysis (PCA) alg orithm to efficiently detect 3D poses of objects in a complex environment. Furthermore, we also present an improved YOLO based 3D object detection algorithm that uses a prioritized heightmap clustering algorithm to handle overlapping bounding boxes. The suggested algorithms have successfully been used at the Artificial-Intelligence Robot Challenge (ARC) 2021 competition with excellent results.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.191_192
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• 2009

지락고장 발생 시 재폐로 동작을 위하여 필요한 조건은 이차아크의 소호 여부이다. 본 논문에서는 1선 지락고장 발생 시 고장상의 전압 파형에 포함되는 짝수 고조파가 이차아크 소호 판단에 유용한지 분석해 보았다. 모의실험은 765kV 모의계통에 아크 지락고장 모델을 적용하여, 다양한 고장 조건하에 이루어졌다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.321-322
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• 2016

본 논문에서는 수배전반에서 부하 설비 또는 외부의 영향을 확인할 수 있는 장치 및 보호설비가 설치되어 있으나 자체 사고를 신속히 검출하고 판단할 수 있는 기술의 확보를 위하여 해상도가 낮은 저가의 열화상 센서에 고속 DSP(Digital Signal Process)를 사용하여 영상 처리 기법인 이차원 보간법 기술을 이용하여 아크플래시에 의해 발생되는 열 특성을 검출하고 검출된 데이터를 전송하여 전기화재사고를 미연에 방지할 수 있는 장치 개발을 위한 기초 특성 연구를 수행하였다.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.546-548
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• 2019

Due to environmental problems, fossil fuel and nuclear power generation are declining and solar power generation is increasing. DC are of a solar power plant is accidents caused by accidents, causing damage to property and people. This study prevents DC are accidents of solar power modules. It is expected that the IoT will be used to quickly alert the manager and greatly contribute to fire prevention.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.460-467
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• 2008

This paper presents a new numerical algorithm for the fault location estimation and arcing fault detection when a single-phase arcing ground fault occurs on a transmission line. The proposed algorithm derived in the spectrum domain is based on the synchronized voltage and current samples measured from the PMUs(Phasor Measurement Units) installed at both ends of the transmission lines. In this paper, the algorithm uses DFT(Discrete Fourier Transform) for estimation. The algorithm uses a short data window for real-time transmission line protection. Also, from the calculated arc voltage amplitude, a decision can be made whether the fault is permanent or transient. The proposed algorithm is tested through computer simulation to show its effectiveness.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2228-2230
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• 2002

The research presented in this paper focuses on a method for the detection of High Impedance Fault(HIF). The method will use the Lifting and neural network system. HIF on the multi-grounded three-phase four-wires primary distribution power system cannot be detected effectively by existing over current sensing devices. These paper describes the application of lifting scheme to the various HIF data. These data were measured in actual 22.9kV distribution system. Wavelet transform analysis gives the frequency and time-scale information. The neural network system as a fault detector was trained to discriminate HIF from the normal status by a gradient descent method. The proposed method performed very well by proving the right state when it was applied staged fault data and normal load mimics HIF, such as arc-welder.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.924-926
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• 1997

A high impedance fault(HIF) in a power system could be due to a downed conductor, and is a dangerous situation because the current may be too small to be detected by conventional means. In this paper, HIF(High impedance fault) and LIF(Low impedance fault) detection methods were reviewed. No single defection method can detect all electrical conditions resulting from downed conductor faults, because high impedance fault have arc phenomena, asymmetry and randomness. Neural network are well-suited for solving difficult signal processing and pattern recognition problem. This paper presents the application of artificial neural network(ANN) to detect the HIF and LIF. Test results show that the neural network was able to identify the high impedance fault by real-time operation. Furthermore, neural network was able to discriminate the HIF from the LIF.