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Power quality diagnosis in distribution networks using support vector regression based S-transform technique

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PL
Analiza jakości energii w sieciach rozdzielczych w oparciu o transformatę S bazującą na regresji wektora wspierającego (SVR)
Języki publikacji
PL
Abstrakty
PL
W artykule przedstawiono nową metodę przeprowadzania automatycznej diagnostyki jakości energii elektrycznej do identyfikacji przyczyn krótkoczasowych zakłóceń napięcia, takich jak zapady napięcia. Zaburzenia napięcia mogą być spowodowane przez długotrwałe lub chwilowe awarie. W proponowanej metodzie diagnozowania jakości zasilania, zastosowano transformatę S do ekstrakcji charakterystyk zarejestrowanych przebiegów z systemu monitoringu. Zastosowano regresję SVR jako technike inteligentna, pozwalajaca na rozróżnienie pomiędzy typami awarii. Wyniki badań wykazały, że proponowana metoda diagnozowania jakości zasilania może zapewnić dokładną diagnozę na temat przyczyn zaburzeń napięcia o krótkim czasie trwania.
EN
This paper presents a novel method for performing automatic power quality diagnosis to identify the causes of short duration voltage disturbances such as voltage sags and swells. Such voltage disturbances can be caused by permanent or non permanent faults. A permanent fault causes permanent damage and power interruption to the customers whereas a non permanent fault can be categorized as either transient or incipient faults. In the proposed power quality diagnosis method, a time frequency analysis technique called as the S-transform is used to analyse and extract features of voltage disturbances recorded from the power quality monitoring system. The support vector regression which is an intelligent technique is then used identify whether the voltage disturbances are caused by permanent, non permanent, transient or incipient faults. Test results proved that the proposed power quality diagnosis method can provide accurate diagnosis on the causes of short duration voltage disturbances.
Rocznik
Strony
38--42
Opis fizyczny
Bibliogr. 19 poz., tab., wykr.
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autor
autor
autor
Bibliografia
  • [1] Bollen, M.H.J, Voltage Recovery After Unbalanced and Balanced Voltage Dips in Three-Phase Systems’, IEEE Trans. on Power Delivery, Vol. 18, no. 4, October (2003), 1376-1381, Digital Object Identifier 10.1109/TPWRD.2003.817725
  • [2] Bollen, M.H.J, Understanding Power Quality Problems: Voltage sag and interruption, IEEE press series on Power Engineering, ISBN 0-7803-4713-7, 179
  • [3] McGranaghan, M.F, Mueller, D.R, Samotyj, M.J, Voltage Sags in Industrial Systems’, IEEE Trans. on Industry Application, Vol. 29, No.2, March/April 1993, 397-403,
  • [4] BS EN 60270:2001/IEC 60270:2000, Edition 3, (2000), ‘High voltage test techniques - Partial discharge measurements’, Section 3.1.
  • [5] Kezunovic, M, Automated Analysis of Voltage Sags, Their Causes and Impacts, IEEE Power Engineering Society Summer Meeting, 2001. ol.2, 15-19, July (2001), 1113–1117
  • [6] Jafarabadi, S.E, Rastegar, H, Contribution to automatic detection and diagnosis of wide variety power quality disturbances using combined wide wavelet transform and neural network methods, Universities Power Engineering Conference, 2004. UPEC2004. 39th International, Vol.2, 6-8 Sept. (2004), 902 – 906.
  • [7] Borghetti, A, Bosetti, M. Di Silvestro, Nucci, C.A, and M. Paolone, M, Continuous-Wavelet Transform for Fault Location in Distribution Power Networks: Definition of Mother Wavelets Inferred from Fault Originated Transients, International Conference on Power Systems Transients (IPST’07) in Lyon, France on 4-7 June, (2007).
  • [8] Azam, M.S, Fang, T, Pattipati, K.R, Rajaiah Karanam, R, A Dependency Model Based Approach for Identifying and Evaluating Power Quality Problems, IEEE Transactions on power delivery, Vol.19, No.3, July (2004), 1154-1166.
  • [9] Il-Yop Chung, Dong-Jun Won, Joong-Moon Kim, Seon-Ju Ahn, Seung-Il Moon, Jang-Cheol Seo, Jong-WoongChoe, Development of power quality diagnosis system for power quality improvement, Power Engineering Society General Meeting, 2003, IEEE Vol. 2, 13-17 July (2003), 12-17
  • [10] Il-Yop Chunga,Dong-Jun Won b, Joong-Moon Kimc, Seon-Ju Ahnd, Seung-Il Moond, Development of a network-based power quality diagnosis system, Electric Power Systems Research 77 (2007) 1086–1094, 25 October (2006)
  • [11] Gargoom, A.M, Eetugrul, N, Soong, W.L, A Comparative study on effective signal processing tools. EPE 2005, (2005), Dresden. ISBN 90-75815-08-5
  • [12] Chuang, C.L, Lu, Y. L, Huang, T. L, Hsiao, Y. T, Jiang, J. A, Recognition of multiple PQ disturbances using wavelet-based neural networks - Part 2: Implementation and applications, IEEE/PES Transmission and Distribution Conference and Exhibition: Asia and Pacific, Dalian, China, Aug. (2005).
  • [13] Bhende, C.N, Mishra, S, Panigrahi, B.K, Detection and classification of power quality disturbances using S-transform and modular neural network, Electric Power Systems Research, 78 (2008) 122–128
  • [14] Pinnegar, C. R, Mansinha, L, The S-Transform with windows of arbitrary and varying shape, Geophysics vol. 68, No.1 (2003), 381-385.
  • [15] Kim, J C, Lee, S.J, Kang, S.H, Evaluation of feeder monitoring parameters for incipient fault detection using Laplace Trend Statistic, IEEE Transaction on industry applications, vol.40, no.60, November/December (2005).
  • [16] Vapnik, V, Cortez, C, Support-vector networks. Machine Learning (1995), pp.273-297. DOI=10.1.1.15.9362
  • [17] Vapnik, V, The Nature of Statistical Learning Theory. Springer, N.Y, ISBN 0387945598, (1995)
  • [18] Vapnik, V, Golowich, S. and Smola, A. Support vector method for function approximation, regression estimation, and signal processing, (1996), DOI: 10.1.1.41.3139.
  • [19] Smola, A.J. and Scholkopf, B, A Tutorial on Support Vector Regression, (1998), Available on-line: http://www.svms.org/regression/SmSc98.pdf
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOB-0042-0008
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