• Journal of Power Electronics
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• v.16 no.3
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• pp.1141-1151
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• 2016

This study proposes a novel islanding detection method for distributed photovoltaic (PV) systems with multi-inverters based on a combination of the power line carrier communication and Sandia frequency shift islanding detection methods. A parameter design method is provided for the novel scheme. On the basis of the designed parameters, the effect of frequency measurement errors and grid line impedance on the islanding detection performance of PV systems is analyzed. Experimental results show that the theoretical analysis is correct and that the novel method with the designed parameters has little effect on the power quality of the inverter output current. Non-detection zones are not observed, and a high degree of reliability is achieved. Moreover, the proposed islanding detection method is suitable for distributed PV systems with multi-inverters.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2113-2118
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• 2016

The precise detection of electric leakage point of underground power cable is very important to reduce cost and time of maintenance and prevent electric shock accident through expedite repair of electric leakage point. This paper proposes a electric leakage point detection system underground power cable using of half-period modulated transmission waveform and earth electric potential measurement. The developed system is composed of transmitter to generate the wanted pulse waveform, receiver to measure and display earth electric potential by the transmitted pulse in electric leakage point and PC Software program to display of GPS coordinate on detection cable line. The performance of the electric leakage point detection system was tested in the constructed underground cable leakage detection test bed. The test results on signal generation voltage precision of signal transmitter, mean detection earth voltage, mean detection leakage current and electric leakage point detection error showed the developed system can be used in electric leakage point detection underground power cable.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.1
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• pp.69-75
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• 2022

Due to the increase in the demand for anomaly detection according to the ageing of power generation facilities, the need for developing an anomaly detection method that can provide high-reliability turbine blade anomaly detection performance has been continuously raised. Additionally, the false detection results caused by a human error accelerates the increase of the need. In this paper, we propose an anomaly detection technique for turbine blades in power plants using deep neural networks. Experimental results prove that the proposed technique achieves stable anomaly detection performance while minimizing human factor intervention.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.111-114
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• 2000

Harmonics and fundamental reactive power of nonlinear loads in serious unbalanced power condition are compensated by current synchronous detection(CSD) theory which is also acceptable for single phase power system, but the CSD theory is not suitable any more in case of controlled independently harmonics and reactive component. Therefore a new algorithm the extended current synchronous detection (ECSD)theory for a three phase active power filter based on decomposition of fundamental reactive distorted components is proposed in this paper. The proposed ECSD theory is simulated and tested comparison with a few power theories under asymmtrical condition in power system.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.6
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• pp.540-549
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• 2015

Unintentional islanding results in safety hazards, reliability, and many other issues. Therefore, the islanding detection of a power conditioning system of a distributed generation, such as a grid-connected photovoltaic inverter, is a key function for standard compliance. Currently, many anti-islanding schemes have been examined, but existing anti-islanding schemes have poor power quality and non-detection zone issues. Therefore, this study analyzes the non-detection zone in a more deliberate manner than the existing analysis of the non-detection zone and proposes a new anti-islanding scheme, which has negligible power quality degradation and no non-detection zone. Simulation and experimental results validate that the proposed scheme shows much better performance than other existing schemes.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.546-557
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• 2022

Recently, the demand to interpret image data with artificial intelligence in various fields is rapidly increasing. Object recognition and detection techniques using deep learning are mainly used, and video integration analysis to determine unstructured object recognition is a particularly important problem. In the case of natural disasters or social disasters, there is a limit to the object recognition structure alone because it has an unstructured shape. In this paper, we propose intelligent video integration analysis system that can recognize unstructured objects based on video turning point and object detection. We also introduce a method to apply and evaluate object recognition using virtual augmented images from 2D to 3D through GAN.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.5
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• pp.410-416
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• 2022

A grid-connected inverter with critical loads uses mode transfer control to supply stable voltage to the load. An islanding detection method should also be used to quickly detect the grid fault and disconnect the inverter from the grid. However using the existing islanding detection method to detect islanding is difficult due to the small fluctuation of the voltage and frequency of the point of common coupling. This study proposes a reactive power P&O islanding detection method by using the positive feedback technique. The proposed method always injects a small variation of reactive power. When a grid fault occurs, the injected reactive power accelerates the reactive power injection reference. As a result, the reactive power reference value and the sensed reactive power become mismatched, and islanding is detected. Reducing the amount of real-time injected reactive power results in high efficiency and power factor. The simulation and experimental results of a 3 kW single-phase inverter are provided to verify the proposed islanding detection method.

• ETRI Journal
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• v.43 no.3
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• pp.549-560
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• 2021

Cyberattacks are often difficult to identify with traditional signature-based detection, because attackers continually find ways to bypass the detection methods. Therefore, researchers have introduced artificial intelligence (AI) technology for cybersecurity analysis to detect malicious PowerShell scripts. In this paper, we propose a feature optimization technique for AI-based approaches to enhance the accuracy of malicious PowerShell script detection. We statically analyze the PowerShell script and preprocess it with a method based on the tokens and abstract syntax tree (AST) for feature selection. Here, tokens and AST represent the vocabulary and structure of the PowerShell script, respectively. Performance evaluations with optimized features yield detection rates of 98% in both machine learning (ML) and deep learning (DL) experiments. Among them, the ML model with the 3-gram of selected five tokens and the DL model with experiments based on the AST 3-gram deliver the best performance.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.77-79
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• 2019

Islanding detection is one of the most important issues for the distributed generation (DG) systems connected to the power grid. The conventional passive islanding detection methods inherently have a non-detection zone (NDZ), and active islanding detection methods may deteriorate the power quality of a power system. This paper proposes a novel hybrid islanding detection method based on Digital Lock-In Amplifier with no NDZ by monitoring the harmonics present in the grid. Proposed method detects islanding by passively monitoring the grid voltage harmonics and verify it by injecting small perturbation for only three-line cycles. Unlike FFT for the harmonic extraction, DLA HC have lower computational burden, moreover, DLA can monitor harmonic in real time, whereas, FFT has certain propagation delay. The simulation results are presented to highlight the effectiveness of the proposed technique. In order to prove the performance of the proposed method it is compared with several passive islanding detection methods. The experimental results confirm that the proposed method exhibits outstanding performance as compared to the conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4661-4680
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• 2016

Cognitive radio (CR) technology is an effective solution to the spectrum scarcity issue. Collaborative spectrum sensing is known as a promising technique to improve the performance of spectrum sensing in cognitive radio networks (CRNs). However, collaborative spectrum sensing is vulnerable to spectrum data falsification (SSDF) attack, where malicious users (MUs) may send false sensing data to mislead other secondary users (SUs) to make an incorrect decision about primary user (PUs) activity, which is one of the key adversaries to the performance of CRNs. In this paper, we propose a coalition based malicious users detection (CMD) algorithm to detect the malicious user in CRNs. The proposed CMD algorithm can efficiently detect MUs base on the Geary'C theory and be modeled as a coalition formation game. Specifically, SSDF attack is one of the key issues to affect the resource allocation process. Focusing on the security issues, in this paper, we analyze the power allocation problem with MUs, and propose MUs detection based power allocation (MPA) algorithm. The MPA algorithm is divided into two steps: the MUs detection step and the optimal power allocation step. Firstly, in the MUs detection step, by the CMD algorithm we can obtain the MUs detection probability and the energy consumption of MUs detection. Secondly, in the optimal power allocation step, we use the Lagrange dual decomposition method to obtain the optimal transmission power of each SU and achieve the maximum utility of the whole CRN. Numerical simulation results show that the proposed CMD and MPA scheme can achieve a considerable performance improvement in MUs detection and power allocation.