• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.05d
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• pp.29-32
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• 2003

Recently, the development of diagnosis technique with high confidence is important on power equipment, for this reason is to use for measurement and analysis of PD with prior appearance of insulation breakdown In this paper, we presents the analysis of trouble signal to use both method of general analysis of $\Phi$-Q-N in PD and statistical parameter by this interpretation Equipment of simulation has made independently DS for 170kV GIS of one phase with same on field. The detected signal through the sensor of Induction and Acoustic Emission is classified which used to characteristic neural network algorithm and then it is analysis.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.2
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• pp.47-54
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• 1986

Hardware implementation of a 32-channel MFC digital receiver has not been easy and simple, because it requires real time processing of PCM data. In this paper, we introduce a method of designing an MFC digital receiver compactly by the channel distribution method. We have implemented the MFC digital receiver to process many cnannels by distributing channels of the TDM input data directly to the commercial digital signal processor chips(NEC uPD7720), and by carrying out the modified Goertzel Algorithm. The design of low cost, reliable, high speed, and compact MFC receiver will be shown.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.679-682
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• 2004

GIS equipment has problems on confidence according to long-time usage, development of diagnosis technique has been importantly recognized accordingly. Therefore. measurement and analysis of PD has been generally used much equipment of GIS. But, in case of measurement of PD at field, real trouble signals are difficult to classify noise. Accordingly, a variety of trouble conditions for DS were simulated, and detected signals were analyzed by the application of electrical and mechanical methods. For this analysis, detected signals were accumulated according to phase-magnitude with the application of Induction sensor, and then we analyzed the characteristics. For the simulation experiment, we made DS for 170kV GIS and analyzed the characteristics of detected signals with the application of neural network algorithm.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1207-1210
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• 2004

Recently, because GIS equipment has problems on confidence according to long-time usage, development of diagnosis technique has been importantly recognized. Therefore. measurement and analysis of PD has been generally used much equipment of GIS. But, in case of measurement of PD at field, real trouble signals are difficult to classify noise. Accordingly, a variety of trouble conditions for DS were simulated, and detected signals were analyzed by the application of electrical and mechanical methods. For this analysis, detected signals were accumulated according to phase-magnitude with the application of Induction sensor, and then we analyzed the characteristics. For the simulation experiment, we made DS for 170kV GIS and analyzed the characteristics of detected singals with the application of neural network algorithm

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.394-396
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• 1992

This note is concerned with the point to point control of manipulators having elastic joints. We present a PD control algorithm which is adaptive with respect to the gravity and elastic parameters of robot manipulators. While the conventional control law is used, a new adaptive law is used to improve the performance. The proposed controller is shown to be stable. It is Shown that steady-state position error converges to zero through some simulations concerning the manipulator with three revolute elastic joints.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.17 no.5
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• pp.567-571
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• 2004

Recently, GIS(Gas Insulated Switchgear) has been recognizing of importance on development of diagnosis technique which is happened problem on confidence for a long time use. Therefore, the measurement and analysis of PD with prior phenomenon of insulation breakdown is used many method of diagnosis for GIS. In this paper, we simulate trouble condition in DS and analysis trouble signal to use electrical and mechanical methods, interpretation of detected signal has analysed with to use ø-q-n pattern and neural network. For this analysis, we have used the induction and AE(acoustic emission) sensors. For the simulation experiment, we make DS for 170 KV GIS and analyze the classification and characteristics of detected signals with the application of neural network algorithm.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2089-2091
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• 2001

In this paper, a hybrid controller that consists of a conventional PD controller and a neural network controller which adapts to various control conditions by online learning is used and a new learning algorithm of the neural networks is used to prevent weights of neural network from diverging. A conventional PI controller and the hybrid controller is applied to speed control of 3 phase induction motor. So in comparison with a PD controller, we prove superiority of hybrid controller by experiments.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1375-1384
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• 2014

Flashover of power transmission line insulators is a major threat to the reliable operation of power system. This paper deals with the flashover prediction of polymeric insulators used in power transmission line applications using the novel condition monitoring technique developed by PD signal time-frequency map and neural network technique. Laboratory experiments on polymeric insulators were carried out as per IEC 60507 under AC voltage, at different humidity and contamination levels using NaCl as a contaminant. Partial discharge signals were acquired using advanced ultra wide band detection system. Salient features from the Time-Frequency map and PRPD pattern at different pollution levels were extracted. The flashover prediction of polymeric insulators was automated using artificial neural network (ANN) with back propagation algorithm (BPA). From the results, it can be speculated that PD signal feature extraction along with back propagation classification is a well suited technique to predict flashover of polymeric insulators.

• Proceedings of the KIEE Conference
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• 2006.07c
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• pp.1511-1512
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• 2006

In this paper, we measured PD signals from artificial surface defects and corona discharges under laboratory circumstances like the fields using both HFCT-sensor and UHF-sensor for MV grade XLPE cable with AMP, spectrum analyzer and oscilloscope. Also, detected signals were analyzed via PRPDA system in this paper. As a result, in this case of HFCT-sensor, the amount of background noise are larger than as compared to UHF-sensor. There is needed some hardware or software or software algorithm removing background noise effectively for diagnosis the underground power transmission cable in the case of HFCT-sensor.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.04a
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• pp.1-4
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• 2004

Recently, because GIS equipment has problems on confidence according to long-time usage, development of diagnosis technique has been importantly recognized. Therefore. measurement and analysis of PD has been generally used much equipment of GIS. But, in case of measurement of PD at field, real trouble signals are difficult to classify noise. Accordingly, a variety of trouble conditions for DS were simulated, and detected signals were analyzed by the application of electrical and mechanical methods. For this anslysis, detected signals were accumulated according to phase-magnitude with the application of Induction sensor, and then we analyzed the characteristics. For the simulation experiment, we made DS for 170kV GIS and analyzed the characteristics of detected singals with the application of neural network algorithm.