• The Transactions of the Korean Institute of Power Electronics
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• v.8 no.2
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• pp.199-210
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• 2003

In this paper, the new Islanding detection method is studied for utility interactive photovoltaic system(UIPVS). It describes the brief of UIPV system and the features of islanding phenomenon. The new islanding detection method for improving the detection characteristics, HCIM(Harmonic Current Injection Method), is proposed and analyzed. The impedance curve of AC load is derived from the complex power equation for testing Islanding detection features. The proposed detection method and the derivation of islanding condition we verified by the simulation with ACSL and the laboratorial experiments.

• Journal of the Korean Solar Energy Society
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• v.26 no.1
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• pp.91-98
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• 2006

Islanding phenomenon of utility-connected photovoltaic power conditioning systems(PV PCS) can cause a variety of problems and must be prevented. If the real and reactive power supplied by PV PCS are closely matched to those of load, islanding detection by passive methods becomes difficult. The active frequency drift(AFD) method, called the frequency bias method, enables islanding detection by forcing the frequency of the voltage in the islanding to drift up or down. In this paper, non-detection zone(NDZ) of AFD is analyzed for the islanding detection method of utility-connected PV PCS by the simulation software tool PSIM.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.364-373
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• 2022

Purpose: The purpose of this paper is to develope smoke detection using AI model for detecting the initial fire in underground utility tunnels using CCTV Method: To improve detection performance of smoke which is high irregular, a deep learning model for fire detection was trained to optimize smoke detection. Also, several approaches such as dataset cleansing and gradient exploding release were applied to enhance model, and compared with results of those. Result: Results show the proposed approaches can improve the model performance, and the final model has good prediction capability according to several indexes such as mAP. However, the final model has low false negative but high false positive capacities. Conclusion: The present model can apply to smoke detection in underground utility tunnel, fixing the defect by linking between the model and the utility tunnel control system.

• 전자공학회논문지 IE
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• v.43 no.2
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• pp.63-69
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• 2006

Islanding phenomenon of utility-connected PV power conditioning systems(PV PCS) can cause a variety of problems and must be prevented. If the real and reactive powers supplied by PV PCS are closely matched to those of load, islanding detection by passive methods becomes difficult. The active frequency drift(AFD) method, called the frequency bias method, enables islanding detection by forcing the frequency of the voltage in the islanding to drift up or down. In this paper, non-detection zone(NDZ) of AFD is analyzed for the islanding detection method of utility-connected PV PCS by simulation tool PSIM.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.719-723
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• 1998

Prevention of the islanding phenomena is one of the most important issues because it can damage electrical equipment connected to the utility system and endanger human life. It is very difficult to detect an islanding condition of a power distribution line with conventional voltage of frequency relays, while the output power and the load power of utility interactive PV inverter units are in nearly balanced state in both active power and reactive power. This paper describes the protective equipment that prevents the PV system connected to the utility grid from starting islanding. Both predictive ocntrol method and harmonic injection method are used for a current control and islanding detection for operating safety.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.252-254
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• 2004

This paper studies power quality problem of utility interconnected distributed generation. Recently, electronic devices that are sensitive to power quality have been increasing. Both utility and customer are interested in power quality problem. Therefore, we studied an effect of utility power quality caused by distributed generation. We detect and analysis voltage sag which is one of power quality indicator. Also, we used Matlab to simulate power quality problem.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.79-95
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• 2012

As credit loan products significantly increase in most financial institutions, the number of fraudulent transactions is also growing rapidly. Therefore, to manage the financial risks successfully, the financial institutions should reinforce the qualifications for a loan and augment the ability to detect a credit loan fraud proactively. In the process of building a classification model to detect credit loan frauds, utility from classification results (i.e., benefits from correct prediction and costs from incorrect prediction) is more important than the accuracy rate of classification. The objective of this paper is to propose a new approach to building a classification model for detecting credit loan fraud based on an individual-level utility. Experimental results show that the model comes up with higher utility than the fraud detection models which do not take into account the individual-level utility concept. Also, it is shown that the individual-level utility computed by the model is more accurate than the mean-level utility computed by other models, in both opportunity utility and cash flow perspectives. We provide diverse views on the experimental results from both perspectives.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.9
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• pp.562-568
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• 2004

The frequency and phase angle of the utility voltage are important in many industrial systems. In the three-phase system, they can be easily known by using the utility voltage vector. However, in the case of single phase system, there are some difficulties in detecting the information of utility voltage. In conventional system, the zero-crossing detection method is widely used, but could not obtain the information of utility voltage instantaneously. In this paper, the new digital PLL control using virtual two phase detector is proposed with a detailed analysis of single-phase digital PLL control for utility connected systems. The experimental results under various utility conditions are presented and demonstrate an excellent phase tracking capability in the single-phase grid-connected operation.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.498-509
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• 2023

Purpose: This paper proposes a method detecting the falling of a maintenance worker in the underground utility tunnel, by applying deep learning techniques using CCTV video, and evaluates the applicability of the proposed method to the worker monitoring of the utility tunnel. Method: Each rule was designed to detect the falling of a maintenance worker by using the inference results from pre-trained YOLOv5 and OpenPose models, respectively. The rules were then integrally applied to detect worker falls within the utility tunnel. Result: Although the worker presence and falling were detected by the proposed model, the inference results were dependent on both the distance between the worker and CCTV and the falling direction of the worker. Additionally, the falling detection system using YOLOv5 shows superior performance, due to its lower dependence on distance and fall direction, compared to the OpenPose-based. Consequently, results from the fall detection using the integrated dual deep learning model were dependent on the YOLOv5 detection performance. Conclusion: The proposed hybrid model shows detecting an abnormal worker in the utility tunnel but the improvement of the model was meaningless compared to the single model based YOLOv5 due to severe differences in detection performance between each deep learning model

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.