• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1997.11a
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• pp.451-455
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• 1997

The partial discharge has been blown as the chief breakdown of power equipments. The analysis and the recognition is much difficult because the partial discharge signal is very small and has complex aging pattern. Recently, insulation aging diagnosis based on pattern of phase(Ф), partial discharge magnitude(q), number(n) has been very important. Owing to depreciate the reappearance of aging progress at the electrical tree pattern and to be difficult to analyze visually, the study on partial discharge pattern is suggested to normalizing analysis method of partial discharge signals. This parer is purposed on prediction of life-time measurement of cv-cable, on decision of risk degree with normalization and real-time measurement of partial discharge signals for aging diagnosis of cv-cable. As normalizing the aging signals of electrical tree in cv-cable, it is able to confirm risk degree of insulation material with the distribution of Ф-q-n and recognize the process of aging pattern using neural network.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.53 no.10
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• pp.515-522
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• 2004

In this study, we tried to investigate that the advantages of SOM(Self Organizing Map) algorithm such as data accumulation ability and the degradation trend trace ability would be adaptable to the analysis of partial discharge pattern recognition. For the purpose, we analyzed partial discharge data obtained from the typical artificial defects in GIS and XLPE power cable system through SOM algorithm. As a result, partial discharge pattern recognition could be well carried out with an acceptable error by use of Kohonen map in SOM algorithm. Also, it was clarified that the additional data could be accumulated during the operation of the algorithm. Especially, we found out that the data accumulation ability of Kohonen map could make it possible to suggest new patterns, which is impossible through the conventional BP(Back Propagation) algorithm. In addition, it is confirmed that the degradation trend could be easily traced in accordance with the degradation process. Therefore, it is expected to improve on-site applicability and to trace real-time degradation trends using SOM algorithm in the partial discharge pattern recognition

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.07a
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• pp.581-584
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• 2000

Generally, it is known that many partial discharges are occurred in one voltage cycle. In this case, it is not easy to distinguish between ultrasonic signals caused by the partial discharge. We describes a pattern of the ultrasonic signal by the partial discharge in transformer. The test setup for ultrasonic signal measurement was to simulate a internal partial discharge by using a needle to plane electrodes. We compared the number of partial discharge and ultrasonic signal in one voltage cycle. The results showed that it was possible to distinguish between ultrasonic signals by analysing partial discharge detection method and ultrasonic signal on time domain.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.544-553
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• 2013

In this paper, we propose the pattern classifier of Radial Basis Function Neural Networks(RBFNNs) for diagnosis of 3-phase partial discharge. Conventional methods map the partial discharge/noise data on 3-PARD map, and decide whether the partial discharge occurs or not from 3-phase or neutral point. However, it is decided based on his own subjective knowledge of skilled experter. In order to solve these problems, the mapping of data as well as the classification of phases are considered by using the general 3-PARD map and PA method, and the identification of phases occurring partial discharge/noise discharge is done. In the sequel, the type of partial discharge occurring on arbitrary random phase is classified and identified by fuzzy clustering-based polynomial Radial Basis Function Neural Networks(RBFNN) classifier. And by identifying the learning rate, momentum coefficient, and fuzzification coefficient of FCM fuzzy clustering with the aid of PSO algorithm, the RBFNN classifier is optimized. The virtual simulated data and the experimental data acquired from practical field are used for performance estimation of 3-phase partial discharge pattern classifier.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.10
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• pp.942-946
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• 2006

We simulated insulation defects of stator winding wire on high voltage generator by 5 types. 4 types have one discharge source and other one has multi discharge source by simulation. For accurate decision, measurements used to PRPD pattern to occurred partial discharge source of various types. In this research, when PRPD pattern carried out or analyzed pattern recognition of discharge source, it used to powerful tools. In this result, PRPD Pattern defined to have single discharge source of 4 types by insulation defect. When insulation defect simulated, all the defected winding have not the same result. Errors for a little different can make mistakes from a subtle distinction. The difference between internal and void discharge have magnitude of pulse amplitude of inner discharge bigger than void discharge and have a shape of bisymmetry. But void discharge has a shape of bisymmetry against maximum value on polarity respectively. In cases of slot and surface discharge, we confirmed to show similar results those other researchers. In case of multi-discharge, as a result of we could classify not perfect match with occurred patterns in single discharge eachother. In the future, we will have to recognize and classify with results of multi-discharge.

• Proceedings of the KIEE Conference
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• 2004.11a
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• pp.251-254
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• 2004

The aging diagnosis technique using partial discharge detection method detects partial discharge signals cause of power equipment failuer and able to forecast the aging state of insulation system through analysis algorithm, in this paper accumulates HFPD signal during constant scheduled cycles to build HFPD pattern and then analyzes HFPD pattern using statistical parameters and ${\Delta}F$ pattern. The 3D pattern is composed of detected signal frequency, amplitude and repeated number and the FRPDA(frequency resolved partial discharge analysis) technique is used in 3D pattern construction. The ${\Delta}F$ pattern shows variation characteristics of amplitude gradient of consecutive HFPD signal Pulses and able to classify discharge types-internal discharge, surface discharge and coronal discharge etc. Fractal mathematics applied to ${\Delta}F$ pattern quantification and neural networks is used in aging diagnostic algorithm.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.05a
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• pp.234-238
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• 1999

The correlation between shape of electrical trees and partial discharge(PD) pulses in low density polyethylene(LDPE) were discussed. We observed growth feature of electrical tree by using optical microscope. On the basis of experimental results of measurements of trees occurring in the needle-plane arrangement with needle shape void and without needle shape void , statistical quantities are derived, which are relevant to PD pulse amplitude and phase. The PD quantities detected by partial discharge detector. we were analyzed q-n distribution pattern and $\psi$ -q-n distribution pattern. In this experiment, electrical trees in the needle-plane arrangement with needle shape void propagated branch type tree and in the needle-plane arrangement without needle shape void propagated bush type tree

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.366-368
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• 2006

This paper analyzes partial discharge pattern using Rogowski coil and acoustic emission sensor to power transformer. Therefore, the location of partial discharge and pattern is deduced as obtaining reliable data to establish core structure in transformer.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1231-1234
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• 2006

In this study, we detected electromagnetic waves generated in an enclosed switchgear and applied various statistical methods for detecting signals. We calculated the various statistical factors via the appropriate statistical methods. Further, we used these statistics to recognize the characteristics for each pattern by identifying the partial discharge in each case for normal, proceeding and abnormal states. The characteristics of electromagnetic wave patterns occurred in various states at electric power facilities and were used as an output variable for more efficient diagnosis. In this paper, we confirmed that the pattern of partial discharge signal can be used as one of the factors used to analyze the insulation state and to consider while estimating diagnosis of insulation states by recognizing the signal pattern to intelligence. We will utilize the proposed diagnosis method to determine insulation degradation states.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.293-300
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• 2014

This paper proposes a novel pattern recognition approach based on the radial basis function (RBF) neural network for identifying insulation defects of high-voltage electrical apparatus arising from partial discharge (PD). Pattern recognition of PD is used for identifying defects causing the PD, such as internal discharge, external discharge, corona, etc. This information is vital for estimating the harmfulness of the discharge in the insulation. Since an insulation defect, such as one resulting from PD, would have a corresponding particular pattern, pattern recognition of PD is significant means to discriminate insulation conditions of high-voltage electrical apparatus. To verify the proposed approach, experiments were conducted to demonstrate the field-test PD pattern recognition of cast resin current transformer (CRCT) models. These tests used artificial defects created in order to produce the common PD activities of CRCTs by using feature vectors of field-test PD patterns. The significant features are extracted by using nonlinear principal component analysis (NLPCA) method. The experimental data are found to be in close agreement with the recognized data. The test results show that the proposed approach is efficient and reliable.