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전력용 변압기의 유중가스 해석을 위한 지능형 진단 알고리즘 개발
임재윤,이대종,이종필,지평식,Lim, Jae-Yoon,Lee, Dae-Jong,Lee, Jong-Pil,Ji, Pyeong-Shik 한국조명전기설비학회 2007 조명·전기설비학회논문지 Vol.21 No.7
IEC code based decision nile have been widely applied to detect incipient faults in power transformers. However, this method has a drawback to achieve the diagnosis with accuracy without experienced experts. In order to resolve this problem, we propose an artificial diagnosis algorithm to detect faults of power transformers using Self-Organizing Feature Map(SOM). The proposed method has two stages such as model construction and diagnostic procedure. First, faulty model is constructed by feature maps obtained by unsupervised learning for training data. And then, diagnosis is performed by compare feature map with it obtained for test data. Also the proposed method usぉms the possibility and degree of aging as well as the fault occurred in transformer by clustering and distance measure schemes. To demonstrate the validity of proposed method, various experiments are unformed and their results are presented. 일반적으로 변압기의 고장진단을 위해 IEC 코드법이 사용되지만, 이 방법은 가스비율이 규정된 범위 내에 존재하지 않거나 경계조건에 있는 경우 숙련된 진단 전문가에게 의뢰하지 않고는 정확한 고장의 원인을 판정하는데 어려움이 있다. 이러한 문제점을 해결하기 위하여 본 논문에서는 SOM을 이용한 전력용 변압기의 고장진단 알고리즘을 제안한다. 제안된 방법은 훈련 데이터의 경쟁학습을 통하여 자기 구성 맵을 구축한 후, 실증 데이터를 구축된 맵에 적용하여 고장의 진단이 이루어진다. 또한 클러스터링 기법에 의해 구축된 정상/고장모델과 정상 데이터를 비교함으로써 고장의 추이 및 열화정도를 분석한다. 제안된 방법의 유용성을 보이기 위한 실험결과에서 기존의 방법들에 비해 향상된 진단결과를 보임을 확인할 수 있었다.
젯슨 나노 기반 활성 함수에 따른 초해상화 알고리즘 성능 분석 연구
임재윤 ( Jae-yoon Lim ),김유민 ( Yu-min Kim ),김용우 ( Yongwoo Kim ) 한국정보처리학회 2022 한국정보처리학회 학술대회논문집 Vol.29 No.1
최근 고해상도 영상이 필요하게 되었으며, 저해상도 영상을 고해상도 영상으로 변환하는 딥러닝 기반의 초해상도 알고리즘에 대한 연구가 활발히 진행되고 있다. 그럼에도 불구하고 딥러닝 기반의 초해상도 알고리즘은 하드웨어의 한계로 인해 임베디드 시스템에서 실행시간이 느린 단점이 있다. 본 논문에서는 심층신경망 기반의 초해상도 알고리즘의 네트워크 구조를 제시하고 다양한 활성화 함수에 따른 화질 및 실행시간 성능을 분석한다. 실험 결과, 젯슨 나노보드의 다양한 활성화 함수 중 화질과 실행 시간의 관계에서 도출한 최적의 활성화 함수가 PReLU 함수임을 확인하였다.
ELM 기반의 지능형 알고리즘과 퍼지 소속함수를 이용한 유입변압기 고장진단 기법
임재윤(Jae-Yoon Lim),이대종(Dae-Jong Lee),지평식(Pyeong-Shik Ji) 대한전기학회 2017 전기학회논문지 P Vol.66 No.4
Power transformers are an important factor for power transmission and cause fatal losses if faults occur. Various diagnostic methods have been applied to predict the failure and to identify the cause of the failure. Typical diagnostic methods include the IEC diagnostic method, the Duval diagnostic method, the Rogers diagnostic method, and the Doernenburg diagnostic method using the ratio of the main gas. However, each diagnostic method has a disadvantage in that it can’t diagnose the state of the power transformer unless the gas ratio is within the defined range. In order to solve these problems, we propose a diagnosis method using ELM based intelligent algorithm and fuzzy membership function. The final diagnosis is performed by multiplying the result of diagnosis in the four diagnostic methods (IEC, Duval, Rogers, and Doernenburg) by the fuzzy membership values. To show its effectiveness, the proposed fault diagnostic system has been intensively tested with the dissolved gases acquired from various power transformers.
유중가스 분석법과 지능형 확률모델을 이용한 유입변압기 고장진단
임재윤(Jae-Yoon Lim),이대종(Dae-Jong Lee),지평식(Pyeong-Shik Ji) 대한전기학회 2016 전기학회논문지 P Vol.65 No.3
It has been proven that the dissolved gas analysis (DGA) is the most effective and convenient method to diagnose the transformers. The DGA is a simple, inexpensive, and non intrusive technique. Among the various diagnosis methods, IEC 60599 has been widely used in transformer in service. But this method cannot offer accurate diagnosis for all the faults. This paper proposes a fault diagnosis method of oil-filled power transformers using DGA and Intelligent Probability Model. To demonstrate the validity of the proposed method, experiment is performed and its results are illustrated.
유입변압기 고장분류를 위한 PNN 기반 Rogers 진단기법 개발
임재윤(Jae-Yoon Lim),이대종(Dae-Jong Lee),지평식(Pyeong-Shik Ji) 대한전기학회 2016 전기학회논문지 P Vol.65 No.4
Stability and reliability of a power system in many respects depend on the condition of power transformers. Essential devices as power transformers are in a transmission and distribution system. Being one of the most expensive and important elements, a power transformer is a highly essential element, whose failures and damage may cause the outage of a power system. To detect the power transformer faults, dissolved gas analysis (DGA) is a widely-used method because of its high sensitivity to small amount of electrical faults. Among the various diagnosis methods, Rogers diagonsis method has been widely used in transformer in service. But this method cannot offer accurate diagnosis for all the faults. This paper proposes a fault diagnosis method of oil-filled power transformers using PNN(Probability Neural Network) based Rogers diagnosis method. The test result show better performance than conventional Rogers diagnosis method.
상관성 분석과 ELM을 이용한 태양광 고장진단 알고리즘 개발
임재윤(Jae-Yoon Lim),지평식(Pyeong-Shik Ji) 대한전기학회 2016 전기학회논문지 P Vol.65 No.3
It is difficult to establish accurate modeling of PV power system because of various uncertainty. However, it is important work to modeling of PV for fault diagnosis. This paper proposes modeling and fault diagnosis method using correlation analysis and ELM(Extreme Learning Machine). Rather than using total data, we select optimal time interval with higher corelation between PV power and solar irradiation. Also, we use average value during 60 minute to avoid rapid variation of PV power. To show the effectiveness of the proposed method, we performed various experiments by dataset.