• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.4
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• pp.553-563
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• 1994

This paper presents a filtering method using neural networks to extract fundamental frequency components of the transient wave signals in power systems. Based on the ability of multilayer feedforward neural networks to approximate any continuous function, a neural networks mapping filter is proposed for the protective distance relaying systems to extract the effective components efficiently. A characteristic feature of this mapping filter is composed of the multilayer perceptron neural networks which are trained by using random signals and those are mapped to the DFT filtering computational structure by GDR(Generalized Delta Rule). The advantage of this approach is demonstrated by the random waves and the fault transient wave signals of EMTP(electromagnetic transients program) in power systems fault conditions. The proposed method is compared with the conventional method and the simulation results show the efficiency of the neural networks.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.9-20
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• 1998

In this paper, a new distance relaying scheme is proposed. Artificial neural networks are applied to the distance relaying system composed of pattern recognition based. The proposed distance relaying scheme has two blocks of pattern recognition stages to estimate the fundamental frequency and to classify the fault types. In the first block, a filtering method using neural networks called a neural networks mapping filter(NMF) is presented to efficiently extract the features. And in the sec'ond block, the estimator called neural networks fault pattern estimator(NFPE) is also presented to classify the fault types by the extracted effective features obtained from NMF. Each block of these applied schemes is trained by back-propagation algorithm of multilayer perceptron and show the fast and accurate pattern recognition by ability of multilayer neural networks. The test result of this approach are obtained the good performance from the fault transient wave signals of EMTP(e1ectromagnetic transients program) in the various fault conditions of power systems.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.193-196
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• 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.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.229-232
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• 2001

This paper presents the protective relaying scheme as a method for discriminating of power transform's transient state associated with magnetizng inrush state, over-exciting state and internal fault using wavelet based neural networs. The simulation of EMTP with respect to different fault, inrush condition and over-exciting condition in transformer have been conducted, and the result prove that the proposed method is able to discriminate between inrush magnetizing current and internal fault.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.871-874
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• 1997

A new typed distance relaying scheme is proposed. Artificial neural networks are applied to the distance relaying system composed of pattern recognition based. The proposed distance relaying scheme have the two block of pattern recognition stages to estimate the fundamental frequency and to classify the fault types. The advantage of this approach is demonstrated by the random waves and the fault transient wave signals of EMTP(electromagnetic transients program) in power systems fault conditions. The proposed method is compared with the conventional method and the simulation results show the efficiency of the neural networks.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.3
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• pp.134-142
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• 2002

This paper presents a new method for the protective relaying scheme in power transformer using wavelet based neural networks. This approach is as fellows. After approximation and detail information is extracted by daub wavelet transform from differential current of power transformer, the former is used for obtaining the rate of differential currents and restrain currents, the latter used as the input of artificial neural networks to avoid the Hiss-operation in over-exciting state and magnetizing inrush state of power transformer. The simulation of EMTP with respect to different faults, inrush conditions and over-exciting conditions in power transformer have been conducted, and the results preyed that the proposed method is able to discriminate magnetizing inrush states, over-exciting stales and internal faults.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.606-609
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• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.5
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• pp.985-993
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• 2001

Neural networks are shown to be effective in being able to distinguish incomplete penetration-like weld defects by directly analyzing the plasma which is generated on each impingement of the laser on the materials. The performance is similar to that of existing methods based on extracted feature parameters. In each case around 93% of the defects in a database derived from 100 artificially produced defects of known types can be placed into one of two classes: incomplete penetration and bubbling. The present method based on classification using plasma is faster, and the speed is sufficient to allow on-line classification during data collection.