• 한국전기전자재료학회:학술대회논문집
• /
• 한국전기전자재료학회 1997년도 추계학술대회 논문집
• /
• pp.451-455
• /
• 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.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2004년도 춘계학술대회 논문집 전기설비전문위원
• /
• pp.109-112
• /
• 2004

In this study, the applicability of SOM(Self Organizing Map) algorithm to partial discharge pattern recognition have been investigated. For the purpose, using acquired data from the artificial defects in GIS, SOM algorithm which has some advantages such as data accumulation ability and the degradation trend trace ability was compared with conventionally used BP(Back Propagation) algorithm. As a result, basically BP algorithm was found out to be better than SOM algorithm. Therefore, it is needed to apply SOM algorithm in combination with BP algorithm in order to improve on-site applicability using the advantages of SOM. Also, for the pattern recognition by use of PRPDA(Phase Resolved Partial Discharge Analysis) it is required the normalization of the PRPDA graph. However, in case of the normalization both BP and SOM algorithm have shown worse results, so that it is required further study to solve the problem.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2004년도 하계학술대회 논문집 C
• /
• pp.1930-1932
• /
• 2004

SOM(Self Organizing Map) algorithm which has some advantages such as data accumulation ability and the degradation trend trace ability was compared with conventionally used BP(Back Propagation) algorithm. For the purpose, partial discharge data were acquired and analysed from the artificial defects in GIS. As a result, basically the pattern recognition rate of BP algorithm was found out to be better than that of SOM algorithm. However, SOM algorithm showed a great on-site-applicability such as ability of suggesting new-pattern-possibility. Therefore, through increasing pattern recognition rate it is possible to apply SOM algorithm to partial discharge analysis. Also, for the image processing method it is required the normalization of the PRPDA graph. However, due to the normalization both BP and SOM algorithm have shown worse results, so that it is required further study to solve the problem.

• 전기학회논문지
• /
• 제67권1호
• /
• pp.114-123
• /
• 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.

• 한국진공학회:학술대회논문집
• /
• 한국진공학회 2012년도 제43회 하계 정기 학술대회 초록집
• /
• pp.149-149
• /
• 2012

We introduce Helicon discharge plasma source and Laser Thomson scattering system recently finished an installation in KRISS. Laser Thomson scattering method is promising for diagnostics in Helicon plasma because a measurement by electrical probe typically used has significant errors due to the gyromotion of electrons induced by high magnetic field. However, we found that LTS is affected by magnetic field so that we applied the normalization method for processing data and the results show a clear Maxwellian distribution at various conditions of magnetic field and RF power at low energy part without distortion.

• 한국전기전자재료학회:학술대회논문집
• /
• 한국전기전자재료학회 1999년도 춘계학술대회 논문집
• /
• pp.161-164
• /
• 1999

Estimation system of aging diagnosis using partial discharge(PD) is being highlighted as a research area for the residual lifetime pridiction of industrial equipment. But the application of PD requires complicated analysis method as expert system because the PD has complex progressing forms according to external stress. In this paper, it has been investigated the statistical distribution to express the 2D PD patterns of the diagnosis system using neural network(NN).

• E2M - 전기 전자와 첨단 소재
• /
• 제11권11호
• /
• pp.1-8
• /
• 1998

This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

• KIEE International Transactions on Power Engineering
• /
• 제3A권4호
• /
• pp.214-221
• /
• 2003

In view of the fact that gas insulated switchgear (GIS) is an important piece of equipment in a substation, it is highly desirable to continuously monitor the state of equipment by measuring the partial discharge (PD) activity in a GIS, as PD is a symptom of an insulation weakness/breakdown. However, since the PD signal is relatively weak and the external noise makes detection of the PD signal difficult, it therefore requires careful attention in its detection. In this paper, the algorithm for detection of PD in the GIS using the wavelet transform (WT) is proposed. The WT provides a direct quantitative measure of the spectral content and dynamic spectrum in the time-frequency domain. The most appropriate mother wavelet for this application is the Daubechies 4 (db4) wavelet. 'db4', the most commonly applied mother wavelet in the power quality analysis, is very well suited to detecting high frequency signals of very short duration, such as those associated with the PD phenomenon. The proposed algorithm is based on utilizing the absolute sum value of coefficients, which are a combination of D1 (Detail 1) and D2 (Detail 2) in multiresolution signal decomposition (MSD) based on WT after noise elimination and normalization.

• Advances in materials Research
• /
• 제6권2호
• /
• pp.169-184
• /
• 2017

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism. This method works by forming of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena can arise and adversely affect the surface integrity of EDMed workpieces. These have to be taken into account and studied in order to optimize the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique that can provide reliable results and readily, be integrated into several technological areas. In this paper, we use an ANN, namely, the multi-layer perceptron and the back propagation network (BPNN) to predict the mean surface roughness of electro-discharge machined surfaces. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for getting a reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components.

• 대한임상독성학회지
• /
• 제7권2호
• /
• pp.156-163
• /
• 2009

Purpose: Doxylamine succinate (DS) is frequently used to treat insomnia and it may induce rhabdomyolysis in the overdose cases. The purpose of this study is to evaluate the factors that can predict the serum creatine kinase (CK) level normalization time for patients with rhabdomyolysis due to DS ingestion. Methods: This study was conducted on 71 patients who were admitted with rhabdomyolysis after DS ingestion during the period from January 2000 to July 2009. Rhabdomyolysis was defined as a serum CK level over 1,000 U/L. The collected data included the general characteristics, the anticholinergic symptoms, the ingested dose, the peak serum CK level, the time interval (TI) from the event to the peak CK level and the TI from the event to a CK level below 1,000 U/L. We evaluated the correlation between the patients' variables and the TI from the event to the peak CK level time and the time for a CK level below 1,000 U/L. Results: The mean ingested dose per body weight (BW) was $30.86{\pm}18.63\;mg/kg$ and the mean TI from the event to treatment was $4.04{\pm}3.67$ hours. The TI from the event to the peak CK level was longer for the patients with a larger ingestion dose per BW (r=0.587, p<0.05). The CK normalization time was longer for the patients with a larger ingested dose per BW (r=0.446, p<0.05) and a higher peak CK level (r=0.634, p<0.05). Conclusion: The ingested dose per BW was correlated with the TI from the event to the peak CK level, and the ingested dose per BW and the peak CK level have significant correlations with the CK normalization time. These factors may be used to determine the discharge period of patients who had rhabdomyolysis following a OS overdose.