• Title, Summary, Keyword: PD features

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Features Extraction and Mechanism Analysis of Partial Discharge Development under Protrusion Defect

  • Dong, Yu-Lin;Tang, Ju;Zeng, Fu-Ping;Liu, Min
    • Journal of Electrical Engineering and Technology
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    • v.10 no.1
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    • pp.344-354
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    • 2015
  • In order to study the development of partial discharge (PD) under typical protrusion defects in gas-insulated switchgear, we applied step voltages on the defect and obtained the ${\varphi}-u$ and ${\varphi}-n$ spectrograms of ultra-high frequency (UHF) PD signals in various PD stages. Furthermore, we extracted seven kinds of features to characterize the degree of deterioration of insulation and analyzed their values, variation trends, and change rates. These characteristics were inconsistent with the development of PD. Hence, the differences of these features could describe the severity of PD. In addition, these characteristics could provide integrated characteristics regarding PD development and improve the reliability of PD severity assessment because these characteristics were extracted from different angles. To explain the variation laws of these seven kinds of parameters, we analyzed the relevant physical mechanism by considering the microphysical process of PD formation and development as well as the distortion effect generated by the space charges on the initial field. The relevant physical mechanism effectively allocated PD severity among these features for assessment, and the effectiveness and reliability of using these features to assess PD severity were proved by testing a large number of PD samples.

Biochemical and molecular features of LRRK2 and its pathophysiological roles in Parkinson's disease

  • Seol, Won-Gi
    • BMB Reports
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    • v.43 no.4
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    • pp.233-244
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    • 2010
  • Parkinson's disease (PD) is the second most common neurodegenerative disease, and 5-10% of the PD cases are genetically inherited as familial PD (FPD). LRRK2 (leucine-rich repeat kinase 2) was first reported in 2004 as a gene corresponding to PARK8, an autosomal gene whose dominant mutations cause familial PD. LRRK2 contains both active kinase and GTPase domains as well as protein-protein interaction motifs such as LRR (leucine-rich repeat) and WD40. Most pathogenic LRRK2 mutations are located in either the GTPase or kinase domain, implying important roles for the enzymatic activities in PD pathogenic mechanisms. In comparison to other PD causative genes such as parkin and PINK1, LRRK2 exhibits two important features. One is that LRRK2's mutations (especially the G2019S mutation) were observed in sporadic as well as familial PD patients. Another is that, among the various PD-causing genes, pathological characteristics observed in patients carrying LRRK2 mutations are the most similar to patients with sporadic PD. Because of these two observations, LRRK2 has been intensively investigated for its pathogenic mechanism (s) and as a target gene for PD therapeutics. In this review, the general biochemical and molecular features of LRRK2, the recent results of LRRK2 studies and LRRK2's therapeutic potential as a PD target gene will be discussed.

Application of Principle Component Analysis and Measurement of Ultra wideband PD signal for Identification of PD sources in Air (기중부분방전원 식별을 위한 광대역 부분방전신호의 측정 및 주성분분석기법의 적용)

  • Lee, K.W.;Kim, M.Y.;Park, D.W.;Shim, J.B.;Chang, S.H.
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • pp.505-506
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    • 2006
  • PD(partial discharge) occurred from variable PD sources in air may be the cause of breakdown in high voltage equipment which affect huge outage in power system. Identification and localization of PD sources is very important for engineer to cope with huge accident beforhand. PD phenomena can be detected by acoustic emission sensor or electromagnetic sensor like antenna. This paper has investigated the identification method using PCA(principal component analysis) for the PD signals from variable PD sources, for which the electric field distribution and PD inception voltages were simulated by using commercial FEM program. PD signals was detected by ultra wideband antenna. Their own features were extracted as the frequency coefficients transformed with FFT(fast fourier transform) and used to obtain independent pincipal components of each PD signals.

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An extensive investigation on gamma ray shielding features of Pd/Ag-based alloys

  • Agar, O.;Sayyed, M.I.;Akman, F.;Tekin, H.O.;Kacal, M.R.
    • Nuclear Engineering and Technology
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    • v.51 no.3
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    • pp.853-859
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    • 2019
  • A comprehensive study of photon interaction features has been made for some alloys containing Pd and Ag content to evaluate its possible use as alternative gamma radiations shielding material. The mass attenuation coefficient (${\mu}/{\rho}$) of the present alloys was measured at various photon energies between 81 keV-1333 keV utilizing HPGe detector. The measured ${\mu}/{\rho}$ values were compared to those of theoretical and computational (MCNPX code) results. The results exhibited that the ${\mu}/{\rho}$ values of the studied alloys are in the same line with results of WinXCOM software and MCNPX code results at all energies. Moreover, Pd75/Ag25 alloy sample has the maximum radiation protection efficiency (about 53% at 81 keV) and lowest half value layer, which shows that Pd75/Ag25 has superior gamma radiation shielding performance among the other compared alloys.

Application of RBFN Using LPC of PD Pulse Shapes for Discriminating Among Multi PD Sources

  • Lee, Kang-Won;Lim, Kee-Joe;Kang, Seong-Hwa
    • KIEE International Transactions on Electrophysics and Applications
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    • v.3C no.5
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    • pp.177-181
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    • 2003
  • Partial discharge pulse shapes from variable PD (partial discharge) sources sustain many characteristics such as types of PD. Ultra high frequency antennas have wide bandwidth from 30KHz to 2㎓. Therefore, signals taken from a UHF antenna have important attributes (rising time, falling time, shape factor, etc.) for electromagnetic sources, such as PD sources. We investigated PD pulse shapes from several PD sources using a UHF antenna and the results were used for classification of PD sources. Features for discrimination are extracted from frequency distribution and LPC (Linear Prediction Coefficient) of time signal. RBFN are used for investigating the possibility of classification of multi-PD sources.

Partial Discharge Ultrasonic Analysis for Generator Stator Windings

  • Yang, Yong-Ming;Chen, Xue-Jun
    • Journal of Electrical Engineering and Technology
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    • v.9 no.2
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    • pp.670-676
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    • 2014
  • The objective of this research is to utilize the ultrasonic method to analyze the property of partial discharge (PD) which is generated by the winding of the insulation stator in the generator. Therefore, a PD measurement system is built based on ultrasonic and virtual instruments. Three types of PD models (internal PD model, surface PD model and slot PD model) have been constructed. With the analysis of these experimental results, this research has identified the ultrasonic signals of the discharges which were produced by three types of PD models. This analysis shows the different features among these PD types. Both the time domain and frequency domain of the ultrasonic signals are obviously different. In addition, an experiment based on a large rotating machine has been done to analyze ultrasonic noises. The result indicates that the ultrasonic noises can be wiped off by the filters and algorithms. The application of this system is convenient for the detection of early signs of insulation failure, which is an effective method for diagnosis of insulation faults.

Discrimination of Air PD Sources Using Time-Frequency Distributions of PD Pulse Waveform (부분방전 펄스파형의 시간-주파수분포를 이용한 기중부분방전원의 식별)

  • Lee Kang-Won;Kang Seong-Hwa;Lim Ki-Joe
    • The Transactions of the Korean Institute of Electrical Engineers C
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    • v.54 no.7
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    • pp.332-338
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    • 2005
  • PD(Partial Discharge) signal emitted from PD sources has their intrinsic features in the region of time and frequency STFT(Short Time Fourier Transform) shows time-frequency distribution at the same time. 2-Dimensional matrices(33$\times$77) from STFT for PD pulse signals are a good feature vectors and can be decreased in dimension by wavelet 2D data compression technique. Decreased feature vectors(13$\times$24) were used as inputs of Back-propagation ANN(Artificial Neural Network) for discrimination of Multi-PD sources(air discharge sources(3), surface discharge(1)). They are a good feature vectors for discriminating Multi-PD sources in the air.

Discrimination of Multi-PD sources using wavelet 2D compression for T-F distribution of PD pulse waveform (부분방전 펄스파형의 시간-주파수분포의 웨이블렛 2D 압축기술을 이용한 복합부분방전원의 식별)

  • Lee, K.W.;Kim, M.Y.;Baik, K.S.;Kang, S.H.;Lim, K.J.
    • Proceedings of the KIEE Conference
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    • pp.1784-1786
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    • 2004
  • PD(Partial Discharge) signal emitted from PD sources has their intrinsic features in the region of time and frequency. STFT(Short Time Fourier Transform) shows time-frequency distribution at the same time. 2-Dimensional matrices(33${\times}$77) from STFT for PD pulse signals are a good feature vectors and can be decreased in dimension by wavelet 2D data compression technique. Decreased feature vectors(13${\times}$24) were used as inputs of Back-propagation ANN(Artificial Neural Network) for discrimination of Multi-PD sources(air discharge sources(3), surface discharge(1)). They are a good feature vectors for discriminating Multi-PD sources.

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A Study of PD Sources Characteristics by Wavelet Transform of Ultrasonic Signals (초음파신호의 웨이블렛변환을 이용한 PD Source별 특징에 관한 연구)

  • Lee, Dong-Zoon;Kwak, Hee-Ro
    • Proceedings of the KIEE Conference
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    • pp.1879-1881
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    • 2003
  • In this paper, acoustic signals in $SF_6$ gas were analyzed using wavelet transform. For this, the PD sources in the $SF_6$ gas were divided into corona discharge surface discharge void discharge and crossing particle and acoustic signals were used to detect the PD sources. The measured signals were time-frequency distribution by wavelet transform and the features were extracted from the PD sources. As a result the characteristics of the PD sources were different. And this results is going to be used for basis diagnosis of $SF_6$ gas insulated apparatus.

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Partial Discharge Pattern Recognition of Cast Resin Current Transformers Using Radial Basis Function Neural Network

  • Chang, Wen-Yeau
    • 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.