• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2000년도 하계학술대회 논문집 A
• /
• pp.366-368
• /
• 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.

• 전자공학회논문지CI
• /
• 제43권4호
• /
• pp.80-87
• /
• 2006

최근 인터넷 응용 프로그램과 관련 기술의 발전에 따라 디지털 멀티미디어 콘텐츠의 보급과 사용이 쉬워지고 있다. 디지털 신호는 복제가 용이하고 복제된 신호는 원신호와 동일한 품질을 갖는다. 이러한 문제점을 해결하고 저작권 보호를 위해 멀티 미디어 핑거프린트가 연구되어지고 있다. 핑거프린팅 기법은 암호학적인 기법들을 이용하여 디지털 데이타를 불법적으로 재배포한 사용자를 찾아냄으로써 디지털 데이타의 저작권을 보호한다. 핑거프린팅 기법은 대칭적이나 비대칭적인 기법과 달리 사용자만이 핑거프린트가 삽입된 데이타를 알 수 있고 데이타가 재배포되기 전에는 사용자의 익명성이 보장되는 기법이다. 본 논문에서는 신경회로망에 의한 공모된 멀티미디어 핑거프린트의 검출 알고리즘을 제안한다. 제안된 알고리즘은 불법공모방지 코드 생성과 에러정정을 위한 신경회로망으로 구성되어 있다. BIBD(Balance Incomplete Block Design) 기반의 불법공모방지 코드는 평균화 선형 공모공격에 대해 100% 공모코드 검출이 이루어졌으며, 에러비트 정정을 위해 (n,k)코드를 사용한 홉필드 신경회로망은 2비트 이내의 에러비트를 정정할 수 있음을 확인하였다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2002년도 하계학술대회 논문집 A
• /
• pp.196-198
• /
• 2002

This paper represents a new approach for the protective relay of power transmission lines using a Artificial Neural Network(ANN). A different fault on transmission lines need to be detected, classified and located accurately and cleared as fast as possible. However, The protection range of the distance relay is always designed on the basis of fixed settings, and unfortunately these approach do not have the ability to adapt dynamically to the system operating condition. ANN is suitable for the adaptive relaying and the detection of complex faults. The backpropagation algorithm based multi-layer perceptron is utilized for the learning process. It allows to make control to various protection functions. As expected, the simulation result demonstrate that this approach is useful and satisfactory.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1999년도 하계학술대회 논문집 B
• /
• pp.856-858
• /
• 1999

The analysis of distribution line faults is essential to the proper protection of power system. A high impedance fault(HIF) dose not make enough current to cause conventional protective device. It is well known that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional Protection system. This paper describes an algorithm using neural network for pattern recognition and detection of high impedance faults. Wavelet transform analysis gives the time-scale information. Time-scale representation of high impedance faults can detect easily and localize correctly the fault waveform.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1998년도 하계학술대회 논문집 G
• /
• pp.2281-2283
• /
• 1998

In this paper, we propose a digital differential protection of power transformer using intelligent schemes. Intelligent schemes is based on fuzzy logic and neural networks. To enhance the distinction between fault and inrush of conventional approaches, relaying technique by fuzzy logic and neural networks are used. We used transformer inrush currents, external and internal fault signals, which are obtained from EMTP simulation.

• International Journal of Control, Automation, and Systems
• /
• 제4권3호
• /
• pp.365-371
• /
• 2006

This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and neural networks for detection and classification of internal faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using ATP/EMTP in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented using toolboxes on MATLAB/Simulink. Various cases and fault types based on Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. It is found that the proposed method gives a satisfactory accuracy, and will be particularly useful in a development of a modern differential relay for a transformer protection scheme.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1995년도 하계학술대회 논문집 B
• /
• pp.606-609
• /
• 1995

This paper presents discriminate between magnetizing inrush and internal faults of power transformer by artificial neural networks trained with preprocessing of fault discriminant. The proposed neural networks contain multi-layer perceptron using back-propagation learning algorithm with logistic sigmoid activation function. For this training and test, we used the relaying signals obtained from the EMTP simulation of model power system. It is shown that the proposed transformer protection system by neural networks never misoperated.

• Nuclear Engineering and Technology
• /
• 제55권6호
• /
• pp.2276-2282
• /
• 2023

We deploy artificial neural networks to unfold neutron spectra from measured energy-integrated quantities. These neutron spectra represent an important parameter allowing to compute the absorbed dose and the kerma to serve radiation protection in addition to nuclear safety. The built architectures are inspired from convolutional neural networks. The first architecture is made up of residual transposed convolution's blocks while the second is a modified version of the U-net architecture. A large and balanced dataset is simulated following "realistic" physical constraints to train the architectures in an efficient way. Results show a high accuracy prediction of neutron spectra ranging from thermal up to fast spectrum. The dataset processing, the attention paid to performances' metrics and the hyper-optimization are behind the architectures' robustness.

• Journal of Radiation Protection and Research
• /
• 제40권1호
• /
• pp.55-64
• /
• 2015

방사선치료계획장치의 핵심기술인 선량분포 계산은 빠르고 정확함을 요구한다. 기존 상용화된 치료계획장치의 선량 계산 방법은 빠르지만 정확성이 부족하고, 몬테칼로 방법은 시뮬레이션 시간과다 문제가 있다. 관심영역의 일부만 몬테칼로 방법이 계산하고 나머지 영역은 비선형함수사상 능력이 뛰어난 신경회로망이 계산하는 시스템은 상대적으로 빠르고 정확한 선량분포를 계산해낼 수 있다. 비균질 매질의 선량분포에 나타나는 불연속점과 변곡점의 특성을 신경회로망이 학습가능 하다는 것을 사전 작업을 통해 확인하였다. 이때 사용된 신경회로망은 Feedforward Multi-Layer Perceptron에 Scaled Conjugated Gradient 알고리즘과 Levenberg-Marquardt 알고리즘으로 각각 학습하여 성능비교를 하였고, 은닉층의 뉴런 개수에 따른 성능비교도 하였다. 마지막으로 균질매질의 팬텀에 대해 상용 치료계획장치의 선량계산 알고리즘으로 계산한 선량분포를 사전작업을 통해 확인된 신경회로망에 학습하여 깊이선량율의 평균제곱오차가 0.00214인 결과를 보여주었다. 균질 및 비균질 매질의 팬텀에 대한 3차원 선량분포를 계산하는 신경회로망 모델 개발 연구가 추가로 진행될 것이다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
• /
• pp.193-196
• /
• 1997

This paper presents the Fault Pattern Estimator(FPE) using the neural network for the protection of the T/L. The proposed FPE has two neural network parts of the fault-types classification and the fault-location estimation. It can detect the fault signals more Quickly and accurately. To prove the performance of the FPE, we have tested using a relaying signals obtained from the EMTP simulations.