• 대한전기학회논문지:전력기술부문A
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• 제50권3호
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• pp.112-119
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• 2001

This paper proposes an adaptive reclosing scheme which is based on the classification of fault patterns. In case that the first reclosing is unsuccessful in distribution system employing with two-shot reclosing scheme, the proposed method can determine whether the second reclosing will be attempted of not. If the first reclosing is unsuccessful two fault currents can be measured before the second reclosing is attempted, where these two fault currents are utilized for an adaptive reclosing scheme. Total harmonic distortion and RMS are used for extracting the characteristics of two fault currents. And the pattern of two fault currents is respectively classified using a mountain clustering method a minimum-distance classifier. Mountain clustering method searches the cluster centers using the acquired past data. And minimum-distance classifier is used for classifying the measured two currents into one of the searched centers respectively. If two currents have the different pattern it is interpreted as temporary fault. But in case of the same pattern, the occurred fault is interpreted as permanent. The proposed method was tested for the fault data which had been measured in KEPCO's distribution system, and the test results can demonstrate the effectiveness of the adaptive reclosing scheme.

• 대한전기학회논문지:전력기술부문A
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• 제50권9호
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• pp.417-423
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• 2001

This paper proposes a technique for fault detection and classification for both LIF(Low Impedance Fault)s and HIF(High Impedance Fault)s using Adaptive Network-based Fuzzy Inference System(ANFIS). The inputs into ANFIS are current signals only based on Root-Mean-Square(RMS) values of 3-phase currents and zero sequence current. The performance of the proposed technique is tested on a typical 154 kV Korean transmission line system under various fault conditions. Test results show that the ANFIS can detect and classily faults including (LIFs and HIFs) accurately within half a cycle.

• KIEE International Transactions on Power Engineering
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• 제3A권4호
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• pp.191-197
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• 2003

Accurate detection and classification of faults on transmission lines is vitally important. In this respect, many different types of faults occur, such as inter alia low impedance faults (LIF) and high impedance faults (HIF). The latter in particular pose difficulties for the commonly employed conventional overcurrent and distance relays, and if undetected, can cause damage to expensive equipment, threaten life and cause fire hazards. Although HIFs are far less common than LIFs, it is imperative that any protection device should be able to satisfactorily deal with both HIFs and LIFs. Because of the randomness and asymmetric characteristics of HIFs, their modeling is difficult and numerous papers relating to various HIF models have been published. In this paper, the model of HIFs in transmission lines is accomplished using the characteristics of a ZnO arrester, which is then implemented within the overall transmission system model based on the electromagnetic transients program (EMTP). This paper proposes an algorithm for fault detection and classification for both LIFs and HIFs using Adaptive Network-based Fuzzy Inference System (ANFIS). The inputs into ANFIS are current signals only based on Root-Mean-Square (RMS) values of 3-phase currents and zero sequence current. The performance of the proposed algorithm is tested on a typical 154 kV Korean transmission line system under various fault conditions. Test results demonstrate that the ANFIS can detect and classify faults including LIFs and HIFs accurately within half a cycle.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 A
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• pp.131-133
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• 1999

This paper proposed the technique of the fault-types classification using Adaptive Network Fuzzy Inference System in the distribution system. Fault and fault-like data in the linear RL load, arc furnace load and converter load were extracted by EMTP. These were characterized into 5 input variables and fuzzified automatically by learning. This technique was tested using another fault data unused learning.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 A
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• pp.252-254
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• 2001

Accurate detection and classification of faults on transmission lines is vitally important. High impedance faults(HIF) in particular pose difficulties for the commonly employed conventional overcurrent and distance relays, and if not detected, can cause damage to expensive equipment, threaten life and cause fire hazards. Although HIFs are far less common than LIFs, it is imperative that any protection device should be able to satisfactorily deal with both HIFs and LIFs. This paper proposes an algorithm for fault detection and classification for both LIFs and HIFs using Adaptive Network-based Fuzzy Inference System(ANFIS). The performance of the proposed algorithm is tested on a typical 154[kV] Korean transmission line system under various fault conditions. Test results show that the ANFIS can detect and classify faults including (LIFs and HIFs) accurately within half a cycle.

• 대한전기학회논문지:전력기술부문A
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• 제51권8호
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• pp.371-378
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• 2002

This paper presents the rapid and accurate algorithm for fault detection and location estimation in the transmission line. This algorithm uses wavelet transform for fault detection and harmonics elimination and utilizes least square error method for fault impedance estimation. Wavelet transform decomposes fault signals into high frequence component Dl and low frequence component A3. The former is used for fault phase detection and fault types classification and the latter is used for harmonics elimination. After fault detection, an adaptive data window technique using LSE estimates fault impedance. It can find a optimal data window length and estimate fault impedance rapidly, because it changes the length according to the fault disturbance. To prove the performance of the algorithm, the authors test relaying signals obtained from EMTP simulation. Test results show that the proposed algorithm estimates fault location within a half cycle after fault irrelevant to fault types and various fault conditions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 추계학술대회 논문집 학회본부
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• pp.372-375
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• 1989

In this paper, a new FTCS with the ability to perform original control objective without considerable loss of control performance in the face of any fault is proposed. The FTCS is composed of two interacting units, Adaptive Controller Unit and Fault Detection/Classification, where ACU performs primary control objective with basic process information(I/O) and environmental information fed by FDU and where FDU detect and classify faults and make decision on remidial action by the use of information provided by ACU.

• 한국지능시스템학회논문지
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• 제15권6호
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• pp.673-681
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• 2005

원자력 발전소는 안정성 및 신뢰성 확보가 가장 중요하므로 고장의 감지 및 진단 시스템의 개발은 원전 자체가 구축하고 있: 다중의 하드웨어 중첩도(hardware redundancy)에도 불구하고 가장 중요한 문제로 취급되고 있다. 본 논문에서는 원저 PWR 증기발생기에서 발생한 고장을 진단하기 위한 알고리듬의 개발을 위해 시스템에서 발생한 고장을 감지하고 분류할 수 있는 ART2 시경회로망 기반 고장진단방법을 제안한다. 고장진단시스템은 발생한 고장을 감지하기 위한 고장감지부, 변화된 시스템파라미터를 추정하기 위한 파라미터 추정부 및 발생한 고장의 종류를 알아내기 위한 고장분류부로 구성된다. 고장분류부는 여러 경계인수를 갖는 ART2(adaptive resonance theory 2) 신경회로망을 이용한 고장분류기로 구성된다. 제안한 고장진단 알고리듬을 증기발생기의 고장진단문제에 적용하여 성능을 확인하였다.

• International Journal of Automotive Technology
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• 제8권5호
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• pp.637-650
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• 2007

A new on-line fault detection and isolation(FDI) scheme has been proposed for engines using an adaptive neural network classifier; this paper investigates the robustness of this scheme by evaluating in a wide range of operational modes. The neural classifier is made adaptive to cope with the significant parameter uncertainty, disturbances, and environmental changes. The developed scheme is capable of diagnosing faults in the on-line mode and can be directly implemented in an on-board diagnosis system(hardware). The robustness of the FDI for the closed-loop system with crankshaft speed feedback is investigated by testing it for a wide range of operational modes, including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all changes occurring simultaneously. The evaluations are performed using a mean value engine model(MVEM), which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.

• 제어로봇시스템학회논문지
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• 제7권6호
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• pp.494-501
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• 2001

In this paper an ANFIS(Adativo Neuro-Fuzzy Inference System)- based fault detection and diagnosis for a closed loop control system is proposed. The proposed diagnostic system contains two ANFIS. One is run as a parallel model within the model in closed loop control(MCL) and the other is run as a series-parallel model within the process in closed loop(PCL) for the generation of relevant symptoms for fault diagnosis. These symptoms are further processed by another classification logic with simple rules and neural network for process and controller fault diagnosis. Experimental results for a DC shunt motor control system illustrate the effectiveness of the proposed diagnostic scheme.