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Study on White Noise Suppression for PD Signals Using ICA

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
PL
Algorytm tłumienia białego szumu towarzyszącemu sygnałowi wyładowania niezupełnego
Języki publikacji
EN
Abstrakty
EN
In this paper, the basic independent component analysis (ICA) model is used to suppress white noise for partial discharge (PD) signals. The mathematical model of ICA is described, and its application in simulation PD signals and PD pulse current signals at HVDC are investigated. The results manifest that the white noise can be effectively suppressed with keeping the most important information of PD signals.
PL
W artykule przedstawiono model niezależnej analizy składowych użyty do tłumienia białego szumu towarzyszącemu sygnałowi wyładowania niezupełnego PD. Opisano model matematyczny i jego zastosowanie w symulacji sygnału PD. Wyniki wskazują, że biały szum może być efektywnie tłumiony przy utrzymaniu wszystkich ważnych informacji sygnału PD.
Rocznik
Strony
252--257
Opis fizyczny
Bibliogr. 36 poz., rys.
Twórcy
autor
autor
autor
autor
autor
  • School of Electrical Engineering, State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, siwenrong@gmail.com
Bibliografia
  • [1] Gulski E., Chojnowski P., Rakowska A., Importance of sensitive on-site testing and diagnosis of transmission power cables, Przegląd Elektrotechniczny, 85 (2009), No. 2, 171-176
  • [2] Noske S., Rakowska A., Siodla K., Measurements of partial discharges as source of management knowledge improvement of the power calbe network, Przegląd Elektrotechniczny, 84 (2008), No. 10, 12-15
  • [3] Ziomek W., Kuffelz E., Sikorski W., Location and recognition of partial discharge sources in power transformer using advanced acoustic emission method, Przegląd Elektrotechniczny, 84 (2008), No. 10, 20-23
  • [4] Szadkowski M., Image of a partial discharge between two conductors which are rapidly changing position against themselves, Przegląd Elektrotechniczny, 84 (2008), No. 10, 203-206
  • [5] Gulski E.,Piepers OM.,Majsymiuk J., Analysis of condition data of high voltage components, Przegląd Elektrotechniczny, 83 (2007), No. 12, 64-68
  • [6] Kurimsky J, Cimbala R., Kolcunova l., Multiscale decomposition for partial discharge analysis, Przegląd Elektrotechniczny, 84 (2008), No. 9, 191-195
  • [7] Boczar T., Lorenc M., The application of the descriptive statistics for recognizing electrical discharge forms registered by the acoustic emission method, Przegląd Elektrotechniczny, 84 (2008), No. 3, 6-69
  • [8] Si Wenrong, Li Junhao, Li Yanming, Time-frequency analysis on pulse current of parital dishcharge, Przegląd Elektrotechniczny, 85 (2009), No. 7, 40-44
  • [9] Morshuis P., Jeroense M., Beyer J., Partial discharge part XXIV: the analysis of PD in HVDC equipment, IEEE Electrical Insulation Magazine, (13) 1997, No. 2, 6-16
  • [10] Si Wenrong, Li Junhao, Li Yanming, Digital detection, grouping and classification of partial discharge signals at DC voltage, IEEE Transactions on Dielectrics and Electrical Insulation, 15 (2008), No. 6, 1663-1664
  • [11] Ma X., Zhou C., Kemp I.J., Interpretation of wavelet analysis and it’s application in partial discharge detection, IEEE Transactions on Dielectrics and Electrical Insulation, 9 (2003), No. 2, 446-457
  • [12] Satish L., Nazneen B., Wavelet denosing of PD signals buried in excessive noise and interference, IEEE Transactions on Dielectrics and Electrical Insulation, 10 (2003), No. 2, 354-367
  • [13] Shetty P.K., Srikanth R., Ramu T.S., Modeling and on-line recognition of PD signal buried in excessive noise, Signal Processing, 84 (2004), No. 12, 2389-2401
  • [14] Chang C. S., Jin J., Kumar S., Su Q., Hoshino T., Hanai M., Kobyashi N., Denoising of partial discharge signals in wavelet packets domain, Proceeding of Electrical Engineering: Scientific Measurement and Technology, 152 (2005), No. 3, 129-140
  • [15] Zhou X., Zhou C., Kemp I. J., An improved methodology for application of wavelet transform to partial discharge measurement denosing, IEEE Transactions on Dielectrics and Electrical Insulation, 12 (2005), No. 3, 586-594
  • [16] Sriram S., Nitin S., Prabhu K.M.M., Bastianns M.J., Signal denosing techniques for partial discharge measurement, IEEE Transactions on Dielectrics and Electrical Insulation, 12 (2005), No. 6, 1182-1191
  • [17] Zhang Hao, Blackburn T.R., Phung B.T., Sen D., A novel wavelet transform technique for on-line partial discharge measurements part2: on-site noise rejection application, IEEE Transactions on Power Delivery, 14 (2007), No. 13, 15-22
  • [18] Zhongrong Xu., Ju Tang., Caixin Sun, Application of complex wavelet transform to suppress white noise in GIS UHF PD signals, IEEE Transactions on Power Delivery, 22 (2007), No. 3, 1498-1504
  • [19] Si Wenrong, LI Junhao, Yuan Peng, LI Yanming, A new approach to extract PD pulse using independent component analysis model, IEEE International Smposium on Electrical Insulation, (2008), 351-354
  • [20] Si Wenrong, LI Junhao, Yuan Peng, LI Yanming, Pulse extraction for partial discharge with double channels with independent component analysis, Journal of Xi’an Jiaotong University (In Chinese), 42 (2008), No. 10, 1263-1268
  • [21] Si Wenrong, LI Junhao, Yuan Peng, LI Yanming, Preliminary study on signal extraction technology for PD pulse based on independent component analysis, High Voltage Engineering (In Chinese), 34 (2008), No. 6, 1277-1282
  • [22] Yang Kanyan, Raijapakse J.C, Denoising of functional MRI using ICA, Proceedings of the 3rd international symposium on Image and Signal Processing and Analysis, (2003), 561-566
  • [23] McKeown. M.J, Yongjie Hu, Wang Z.J, ICA denosing for eventrelated fMRI studies, IEEE-EMBS.27th Annual International Conference of the Engineering in Medicine and Biology Society, (2005), 157-161
  • [24] James V.S, Independent component analysis: a tutorial introduction, Cambridge, Mass MIT Press, 2004
  • [25] Aapo Hyvarinen, Juha Karhunen and Erkki Oja, Independent component analysis, NewYork, A Wiley-Interscience Publication, John Wiley & Sons, Inc. (USA), 2001
  • [26] Cardoso J.F., High-order contrasts for independent component analysis, Neural Computation, 11 (1999), No. 1, 157-192
  • [27] Cardoso J.F., Informax and maximum likelihood for blind source separation, IEEE Signal Processing Letter, 4 (1997), No. 4, 112-114
  • [28] Dezhong Yao, Huafu Chen, Becher. S, Tiangang Zhou, Yan Zhuo and Lin Chen, A fMRI data analysis method using a fast Infomax-based ICA algorithm, Canadian Conference on Electrical and Computer Engineering, (2001), 1105-1110
  • [29] Tang H.H., Amari S.I., Adaptive on-line learning algorithms for blind separation: Maximum entropy and minimum mutual information, Neural Networks, 9 (1997), No. 67, 1457-1481
  • [30] Deco G., Information maximization and ICA. Is there a difference?, Neural Computation, 10 (1998), No. 8, 2085-2101
  • [31] Hyvarinen A., New approximations of differential entropy for independent component analysis and projection purist, Advances in Neural Information Processing Systems, 10 (1998), No. 3, 273-279
  • [32] Hyvarinen A., Independent component analysis: algorithm and application, Neural Network, 13 (2000), No. 4, 411-430
  • [33] Hyvarinen A., Fast and robust fixed-point algorithm for ICA, IEEE Transactions on Neural Network, 10 (1999), No. 3, 626- 634
  • [34] Yang Fusheng and Hong Bo, The theory and application of independent component analysis, Tsinghua University Press, (in Chinese) Beijing, 2006
  • [35] Si Wenrong, LI Junhao, Yuan Peng, LI Yanming, Feature extraction methods for time frequency energy distribution of PD pulse, International Conference on Condition Monitoring and Diagnosis (2008), 82-84
  • [36] Si Wenrong, LI Junhao, Yuan Peng, LI Yanming, Study on time-frequency characteristic of PD Pulse using Wigner-Ville Distributions, IEEE International Smposium on Electrical Insulation, (2008), 355-357
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOK-0026-0060
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