• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1828-1830
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• 2003

Partial discharge(PD) tests are used to evaluate the insulation condition of stator windings in two 4.16kV and three 6.6kV motors. These tests were conducted using a conventional partial discharge detector(PDD) and turbine generator analyzer(TGA). Off-line PD measurements were performed on five high voltage motors. PD magnitudes ranged from 1000 pC to 5400 pC at the normal line-to-ground voltage. Five high voltage motors have been equipped with 80pF epoxy-mica coupler on the motor terminal box. The PD pulse from sensors were measured with the TGA instrument. TGA summarizes each plot with two Quantities such as the peak PD magnitude(Qm) and the total PD activity(NQN). The defect mechanisms of high voltage motor can be associated with PD patterns such as internal, slot and conductor surface discharges. The PDD and TGA test results of No. 4 motor showed that internal discharge was detected in voids of the groundwall insulation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.7
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• pp.41-47
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• 2014

The on-line partial discharge (PD) in stator windings of air-cooled gas turbine (GT) generator (119.2MVA, 13.8kV) is measured and analyzed in this paper. This generator was designed by global vacuum pressure impregnation (VPI). The generator failed two times at top bar (16T) of phase B in the stator slot. Six epoxy-mica capacitors were installed in three phases of GT generator. On-line PD test was performed on GT generator using turbine generator analyzer (TGA). TGA showed that the normalized quantity number (NQN) and the PD magnitude($Q_m$) were high in phase B. Internal discharges were generated in phases A, B and C. The trend analysis of NQN and $Q_m$ value are obtained in order to monitor the insulation condition in GT generator stator windings. Phases A and C were in good condition. But phase B had deteriorated significantly

• Economic and Environmental Geology
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• v.40 no.5
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• pp.661-673
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• 2007

Rock materials of the three-storied stone pagoda in the Cheongryongsa temple in Korea are mainly composed of gneissose two-mica granite and fine-grained granite. This stone pagoda shows structural instability due to cracks and breaking-out of the stones. The surface properties of the stone is highly degraded by various inorganic pollutants and epilithic biospecies. Therefore, this study carried out comprehensive deterioration diagnosis by non-destructive methods, and some conservation treatments base on the diagnosis were carried out to reduce weathering progress. As the treatments, the biospecies and lichen that covering on the stone surfaces were removed by dry and wet cleaning, and degraded concrete applied to the pagoda for restoration in the past was removed and repaired with epoxy resin. Oxidized iron plates inserted between the rock properties were also substituted titanium stainless steels. After all processes are completed, we sprayed consolidant on the rock surface. Finally, the ground of the stone pagoda was rearranged using small rock aggregates, and the fence was established for control of artificial deterioration by visitors and environmental maintenance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1392-1401
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• 2017

In this study, the design methodology of pattern classification is introduced for avoiding faults through partial discharge occurring in the power facilities and local sites. In order to classify some partial discharge types according to the characteristics of each feature, the model is constructed by using the Radial Basis Function Neural Networks(RBFNNs) and Particle Swarm Optimization(PSO). In the input layer of the RBFNNs, the feature vector is searched and the dimension is reduced through Principal Component Analysis(PCA) and PSO. In the hidden layer, the fuzzy coefficients of the fuzzy clustering method(FCM) are tuned using PSO. Raw datasets for partial discharge are obtained through the Motor Insulation Monitoring System(MIMS) instrument using an Epoxy Mica Coupling(EMC) sensor. The preprocessed datasets for partial discharge are acquired through the Phase Resolved Partial Discharge Analysis(PRPDA) preprocessing algorithm to obtain partial discharge types such as void, corona, surface, and slot discharges. Also, when the amplitude size is considered as two types of both the maximum value and the average value in the process for extracting the preprocessed datasets, two different kinds of feature datasets are produced. In this study, the classification ratio between the proposed RBFNNs model and other classifiers is shown by using the two different kinds of feature datasets, and also we demonstrate the proposed model shows superiority from the viewpoint of classification performance.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.10
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• pp.965-975
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• 2005

In the case of the fault in stator windings of a high voltage motor. it facilitates certain destructive characteristics in insulations. This will result in a decreased reliability in power supplies and will prevent the generation of electricity, which will result in huge economic losses. This study simulates motor windings using normal windings and four faulty windings for an actual fault in stator winding of a high voltage motor. The partial discharge signals produced in each faulty winding were measured using an 80 PF epoxy/mica coupler sensor. In order to quantified signal waves its a way of feature extraction for each faulty signal, the signal wave of winding was quantified to measure the degree of skewness shape and kurtosis, which are both types of statistical parameters, using a discrete wavelet transformation method for each faulty type. Wave types present different types lot each faulty type, and the skewness and kurtosis also present different quantified values. The result of feature extraction was used as a preprocessing stage to identify a certain fault in stater windings. It is evident that the type of faulty signals can be classified from the test results using faulty signals that were randomly selected from the signal, which was not applied in the training after the training and learning period, by applying it to a back-propagation algorithm due to the supervising and learning method in a neural network in order to classify the faulty type. This becomes an important basis for studying diagnosis methods using the classification of faulty signals with a feature extraction algorithm, which can diagnose the fault of stator windings in the future.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.