• Journal of the Institute of Convergence Signal Processing
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• v.12 no.3
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• pp.212-216
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• 2011

Fault detection technique for photovoltaic power systems is significant to dramatically reduce economic damage in industrial fields. This paper presents a novel fault detection approach using Fourier neural networks and stochastic decision making strategy for photovoltaic systems. We achieve neural modeling to represent its nonlinear dynamic behaviors through a gradient descent based learning algorithm. Next, a general likelihood ratio test (GLRT) is derived for constructing a decision malling mechanism in stochastic fault detection. A testbed of photovoltaic power systems is established to conduct real-time experiments in which the DC power line communication (DPLC) technique is employed to transfer data sets measured from the photovoltaic panels to PC systems. We demonstrate our proposed fault detection methodology is reliable and practicable over this real-time experiment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.7
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• pp.925-930
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• 2013

The fault current of the power transmission system is greater than that of the power distribution system. Therefore, the introduction of superconducting fault current limiter (SFCL) is more needed to reduce the increased fault current. The trigger-type SFCL consists of the high-temperature superconducting element (HTSC), the current limiting reactor (CLR) and the circuit breaker (CB). The trigger-type SFCL can be used to supplement the disadvantages of the resistive-type SFCL. The operation characteristics of the current ratio differential relay which is usually applied to the protection device of the power transmission system are expected to be affected under fault conditions and the applicability of the trigger-type SFCL. In this paper, we analyzed the operating characteristics, by the fault conditions, between the current ratio differential relay for line protection and the trigger-type SFCL in the power transmission system through the PSCAD/EMTDC simulation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.4
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• pp.118-123
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• 2004

This paper studies islanding detection of DG(distributed generation) considering fault location. Through the past studies, we found that islanding detection has been studied that DG disconnected when power islanding was detected by power state change and output change of DG. But, fault location was not considered. For example, fault in adjacent distribution line, fault on interconnection line fault, load shave by overload and normal operation were not considered. In this paper, We distinguish these considerations through power state change. Also, we proved islanding detection algorithm through PSCAD/EMTDC simulation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.3
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• pp.376-382
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• 2014

E-Actuator is an essential part of an eVGT, it receives the command from the main ECU and controls the vane. An e-Actuator failure can cause an abrupt change in engine output and it may induce an accident. Therefore, it is required to detect anomalies in the e-Actuator in real time to prevent accidents. In this paper, an e-Actuator fault detection method using on-line parameter identification is proposed. To implement on-line fault detection algorithm, many constraints are considered. The test input and sampling rate are selected considering the constraints. And new recursive system identification algorithm is proposed which reduces the memory and MCU power dramatically. The relationship between the identified parameters and real elements such as gears, spring and motor are derived. The fault detection method using the relationship is proposed. The experiments with the real broken gears show the effectiveness of the proposed algorithm. It is expected that the real time fault detection is possible and it can improve the safety of eVGT system.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.366-368
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• 2000

When faults occur in transmission lines, the classification of faults is very important. If the fault is HIF(High Impedance Fault), it cannot be detected or removed by conventional overcurrent relays (OCRs), and results in fire hazards and causes damages in electrical equipment or personal threat. The fast discrimination of fault needs to effective protection and treatment and is important problem for power system protection. This paper proposes the fault detection and discrimination algorithm for LIFs(Low Impedance Faults) and HIFs(High Impedance Faults). This algorithm uses artificial neural networks and variation of 3-phase maximum currents per period while faults. A double lines-to-ground and line-to-line faults can be detected using Neural Network. Also, the other faults can be detected using the value of variation of maximum current. Test results show that the proposed algorithms discriminate LIFs and HIFs accurately within a half cycle.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.354-356
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• 2006

This paper presents a new one-terminal numerical algorithm for fault location estimation and for faults recognition. The proposed algorithm are derived for the case of most frequent single-phase line to ground fault in the time domain. The arc voltage wave shape is modeled numerically on the basis of a great number of arc voltage records obtained by transient recorder. From the calculated arc voltage amplitude it can make a decision whether the fault is permanent of transient. In this paper the algorithm uses a very short data window and enables fast fault detection and classification for real-time transmission line protection. To test the validity of the proposed algorithm the Electro-Magnetic Transient Program(EMTP/ATP) is used.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.233-238
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• 1993

In this paper, two diagnosis algorithms using the simple fuzzy, cognitive map (FCM) that is an useful qualitative model are proposed. The first basic algorithm is considered as a simple transition of Shiozaki's signed directed graph approach to FCM framework. And the second one is an extended version of the basic algorithm. In the extension, three important concepts, modified temporal associative memory (TAM) recall, temporal pattern matching algorithm and hierarchical decomposition are adopted. As the resultant diagnosis scheme takes short computation time, it can be used for on-line fault diagnosis of large scale and complex processes that conventional diagnosis methods cannot be applied. The diagnosis system can be trained by the basic algorithm and generates FCM model for every experienced process fault. In on-line application, the self-generated fault model FCM generates predicted pattern sequences, which are compared with observed pattern sequences to declare the origin of fault. In practical case, observed pattern sequences depend on transport time. So if predicted pattern sequences are different from observed ones, the time weighted FCM with transport delay can be used to generate predicted ones. The fault diagnosis procedure can be completed during the actual propagation since pattern sequences of tvo different faults do not coincide in general.

• Journal of Power Electronics
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• v.16 no.6
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• pp.2258-2271
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• 2016

In this paper, some crucial performance characteristics related to the operational reliability of the post-fault Pulse Width Modulated (PWM) rectifiers, such as line current harmonic distortion, Common Mode Voltage (CMV), and current stress on the capacitors, are fully investigated. The aforementioned performance characteristics of post-fault rectifiers are highly dependent on the utilized space vector modulation (SVM) schemes, which are also examined. Detailed analyses of the three most commonly used SVM schemes for post-fault PWM rectifiers are provided, revealing the major differences in terms of the zero vector synthesis approaches. To compare the performances of the three SVM schemes, the operating principles of a post-fault rectifier are presented with various SVM schemes. Using analytical and numerical methods in the time domain, the performances of the line current distortion, common mode voltage and capacitor current are evaluated and compared for each SVM scheme. The proposed analysis demonstrates that the zero vector synthesis approaches of the considered methods have significant impacts on the performance characteristics of rectifiers. In addition, the advantages and disadvantages of the proposed SVM schemes are discussed. The experimental results verify the effectiveness and validity of the proposed analysis.

• Proceedings of the SAREK Conference
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• 2007.11a
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• pp.88-94
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• 2007

This study deals with on-line fault detection and diagnosis for heat exchanger of variable speed refrigeration system. Conventional studies about fault of heat exchanger in refrigeration system have used temperature and pressure information. The temperature and pressure are able to be used valuably for faults detection of constant speed refrigeration system. However in case of variable speed refrigeration system, the temperature and pressure are no longer useful information for fault detection due to compensation effect of feedback controller. While current information is possible to detect faults of variable speed refrigeration system. The current information was detected in an inverter, it was used after transforming rms value. The faults of variable speed refrigeration system are divided into electrical faults and mechanical faults. We performed fault detection and diagnosis about heat exchanger among mechanical faults such as condenser fouling and evaporator fan fouling through some experiments.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.33 no.1
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• pp.65-69
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• 2020

The aim of this study is to improve the fault decision ability of FRTU (Feeder remote terminal unit) in DAS (Distribution automation system). FRTU uses the FI (Fault indicator) algorithm based on fault current pickup and operation of the protection device. Even if the inrush current flows or the protection device is sensitive to the transient current, FRTU may indicate incorrect fault information. To address these problems, we propose an improved fault recognition algorithm that can be applied to FRTU. We will detect a specific wave that is indicative of a fault, and use this information to identify a fault wave. The specific wave-detection algorithm is based on the duration and periodicity of the voltage, current, and harmonic variations. In addition, we propose fault recognition algorithms using voltage factor variation analysis and DWT (Discrete wavelet transform). All the wave data used in this study were actual data stored in FRTU.