• Journal of Electrical Engineering and Technology
• /
• 제12권6호
• /
• pp.2118-2126
• /
• 2017

The large-scale power system blackouts have indicated that conventional protection relays that based on local signals cannot fit for modern power grids with complicated setting or heavily loaded-flow transfer. In order to accurately detect various faulted lines and improve the fault-tolerance of wide-area protection, a novel multi-section weighed fault matching and detecting algorithm is proposed. The real protection vector (RPV) and expected section protection vectors (ESPVs) for five fault sections are constructed respectively. The function of multi-section weighed fault matching is established to calculate the section fault matching degrees between RPV and five ESPVs. Then the fault degree of protected line based on five section fault degrees can be obtained. Two fault detecting criterions are given to support the higher accuracy rate of detecting fault. With the enumerating method, the simulation tests illustrate the correctness and fault-tolerance of proposed algorithm. It can reach the target of 100% accuracy rate under 5 bits error of wide-area protections. The influence factors of fault-tolerance are analyzed, which include the choosing of wide-area protections, as well as the topological structures of power grid and fault threshold.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.714-717
• /
• 1996

This paper presents a fault detection and diagnosis methodologies based on weighted symptom model and pattern matching between the coming fault propagation trend and the simulated one. At the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom model(WSM) is used to generate those candidates. The weight is determined from dynamic simulation. Using WSMs, the methodology can generate the cause candidates and rank them according to the probability. Secondly, the fault propagation trends identified from the partial or complete sequence of measurements are compared to the standard fault propagation trends stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The standard trends have been generated using dynamic simulation and stored a priori. The proposed methodology has been illustrated using two case studies and showed satisfactory diagnostic resolution.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
• /
• pp.203-207
• /
• 1998

In this paper, it is shown how Fuzzy Pattern Matching can be applied to diagnosis of the most common faults of Rotating Machinery. The whole diagnosis process has been divided in three steps : Fault Detection, Fault Isolation and Fault Identification, whose possible results are described by linguistic patterns. Diagnosis will consist in obtaining a set of matching indexes that indexes that express the compatibility of the fuzzified features extracted from the measured vibration signals, with the knowledge contained in the corresponding patterns.

• 제어로봇시스템학회논문지
• /
• 제3권5호
• /
• pp.520-525
• /
• 1997

This paper presents a fault detection and diagnosis methodology based on weighted symptom model and pattern matching between the coming fault propagation trend and the simulated one. In the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom model is used to generate those candidates. The weight is determined from dynamic simulation. Using WSM, the methodology can generate the cause candidates and rank them according to the probability. Second, the fault propagation trends identified from the partial or complete sequence of measurements are compared with the standard fault propagation trends stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The standard trends have been generated using dynamic simulation and stored a priori. The proposed methodology has been illustrated using two case studies, and the results showed satisfactory diagnostic resolution.

• 한국정보과학회논문지:시스템및이론
• /
• 제35권2호
• /
• pp.60-65
• /
• 2008

그래프의 매칭 배제 집합은 그것을 삭제한 그래프가 완전 매칭이나 준완전 매칭을 가지지 않는 에지 집합이다. 매칭 배제수는 모든 매칭 배제 집합의 최소 크기이다. 이 논문에서는 임의의 $m{\geq}4$에 대하여 H-차원 제한된 HL-그래프와 재귀원형군 $G(2^m,4)$의 매칭 배제수는 분지수 m과 같고, 모든 최소 매칭 배제 집합은 한 정점에 인접한 에지 집합임을 보인다.

• 한국통신학회논문지
• /
• 제32권4A호
• /
• pp.379-386
• /
• 2007

본 논문에서는 효율적인 다중 고착 고장 진단 알고리듬을 제안하겠다. 제안하는 고장 진단 알고리듬은 완전일치공통부분을 고장 진단의 중요한 기준으로 사용함으로써 단일 고착 고장 시뮬레이터 환경에서도 다중 고착 고장을 진단할 수 있다. 또한 각 고장간의 식별성을 높여 다중 고착 고장을 진단함에도 불구하고, 고장 후보의 수를 획기적으로 줄일 수 있었다. 이를 위하여 출력단의 수에 따른 가중치 개념과 가산, 감산 연산을 사용하였다. 이 알고리듬은 ISCAS85회로와 완전 주사 스캔이 삽입된 ISCAS89회로에서 실험하여 성능을 입증하였다.

• 한국압력기기공학회 논문집
• /
• 제13권1호
• /
• pp.48-53
• /
• 2017

We diagnosed the defect using the data obtained from the nuclear power plant simulator. In this paper, we diagnosed faults in the nuclear power plant system for discovery instead of the traditional single-component or device unit. We created the six fault scenarios and used a fault simulator to obtain the fault data. It was extracted pattern from acquired failure data. Neural network model was trained and simple pattern matching algorithm was applied. We presented a simulation result and confirmed that the applied algorithm works correctly.

• Korean Chemical Engineering Research
• /
• 제58권3호
• /
• pp.408-415
• /
• 2020

본 연구에서는 auto-encoder와 self-organizing map을 결합한 auto-encoder with self-organizing map(AE-SOM) 기법을 이용하여 EVA 생산공정의 이상을 검출 및 진단하였고, Granger의 인과분석을 통해 이상 검출 데이터의 이상 전파 방향을 확인하였다. 분석 데이터는 1년 7개월 간의 조업데이터를 이용하였으며, autoclave 반응기의 조업 변수를 주로 분석하였다. 데이터 전처리 과정에서 데이터의 표준화를 먼저 진행하고, 조업의 각 grade의 sample 수를 동일하게 200개 임의로 추출하였다. 이후 AE-SOM을 적용하여 각 grade의 best matching unit (BMU)를 도출하였다. 각각의 BMU를 기준으로 조업 데이터가 얼마나 벗어났는지를 기준으로 데이터의 이상을 판별하였다. 공정 이상이 발견될 시 이상원인을 contribution plot을 이용하여 확인하였고 이상원인 변수의 인과성을 Granger의 인과분석을 통해 분석하였다. 그 결과 조업 시 발생한 2번의 셧다운의 전조를 모두 검출하였으며 이상이 발생한 원인변수에서 기인한 공정 이상의 전파 방향을 분석하였다.

• Journal of Power Electronics
• /
• 제19권2호
• /
• pp.443-453
• /
• 2019

As the core component of transmission systems, converters are very prone to failure. To improve the accuracy of fault diagnosis for wind power converters, a fault feature extraction method combined with a wavelet transform and compressed sensing theory is proposed. In addition, an improved AdaBoost-SVM is used to diagnose wind power converters. The three-phase output current signal is selected as the research object and is processed by the wavelet transform to reduce the signal noise. The wavelet approximation coefficients are dimensionality reduced to obtain measurement signals based on the theory of compressive sensing. A sparse vector is obtained by the orthogonal matching pursuit algorithm, and then the fault feature vector is extracted. The fault feature vectors are input to the improved AdaBoost-SVM classifier to realize fault diagnosis. Simulation results show that this method can effectively realize the fault diagnosis of the power transistors in converters and improve the precision of fault diagnosis.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
• /
• pp.860-863
• /
• 1997

FCM(Fuzzy Cognitive Map) is a fuzzy signed directed graph for representing causal reasoning which has fuzziness between causal concepts. Authors have already proposed FCM-based fault diagnostic scheme. However, the previously proposed scheme has the problem of lower diagnostic resolution. In order to improve the diagnostic resolution, a new diagnostic scheme based on extended FCM which incorporates the concept of fuzzy number into FCM is developed in this paper. Furthermore, an enhanced TAM(Temporal Associative Memory) recall procedure and pattern matching scheme are also proposed.