메뉴 건너뛰기




Volumn 39, Issue 2, 2013, Pages 302-309

Combinational diagnosis for transformer faults based on multi-models

Author keywords

Bayes classification; Changeable weights; Combinational diagnosis; Fault diagnosis; Model group; Support vector machine; Transformer fault

Indexed keywords

BAYES CLASSIFICATION; CHANGEABLE WEIGHT; FAULT DIAGNOSIS MODEL; MODEL GROUPS; NAIVE BAYES MODELS; PRACTICAL PROJECTS; TRANSFORMER FAULTS; TREE AUGMENTED NAIVE BAYES;

EID: 84875080136     PISSN: 10036520     EISSN: None     Source Type: Journal    
DOI: 10.3969/j.issn.1003-6520.2013.02.007     Document Type: Article
Times cited : (26)

References (20)
  • 1
    • 79951596577 scopus 로고    scopus 로고
    • Application of D-S evidence deducing theory to one kind of mechanical fault diagnosis
    • Tian H X, Sun Y L, Liu S Y. Application of D-S evidence deducing theory to one kind of mechanical fault diagnosis[J]. Applied Mechanics and Materials, 2011, 48(49): 249-252.
    • (2011) Applied Mechanics and Materials , vol.48 , Issue.49 , pp. 249-252
    • Tian, H.X.1    Sun, Y.L.2    Liu, S.Y.3
  • 2
    • 45949084922 scopus 로고    scopus 로고
    • Condition assessment for power transformers by Bayes networks
    • ZHAO Wenqing, ZHU Yongli, JIANG Bo, et al. Condition assessment for power transformers by Bayes networks[J]. High Voltage Engineering, 2008, 34(5): 1032-1039.
    • (2008) High Voltage Engineering , vol.34 , Issue.5 , pp. 1032-1039
    • Zhao, W.1    Zhu, Y.2    Jiang, B.3
  • 3
    • 84864515116 scopus 로고    scopus 로고
    • Transformer fault diagnosis using self-adaptive RBF neural network algorithm
    • FU Qiang, CHEN Tefang, ZHU Jiaojiao. Transformer fault diagnosis using self-adaptive RBF neural network algorithm[J]. High Voltage Engineering, 2012, 38(5): 1368-1375.
    • (2012) High Voltage Engineering , vol.38 , Issue.5 , pp. 1368-1375
    • Fu, Q.1    Chen, T.2    Zhu, J.3
  • 4
    • 84864510253 scopus 로고    scopus 로고
    • Transformer fault diagnosis using improved artificial fish swarm with rough set algorithm
    • CHEN Xiaoqing, LIU Juemin, HUANG Yingwei, et al. Transformer fault diagnosis using improved artificial fish swarm with rough set algorithm[J]. High Voltage Engineering, 2012, 38(6): 1403-1409.
    • (2012) High Voltage Engineering , vol.38 , Issue.6 , pp. 1403-1409
    • Chen, X.1    Liu, J.2    Huang, Y.3
  • 5
    • 80155148743 scopus 로고    scopus 로고
    • Guide for condition evaluation of oil-immersed power transformers (reactors)
    • Q/GDW 169-2008
    • Q/GDW 169-2008 Guide for condition evaluation of oil-immersed power transformers (reactors)[S], 2008.
    • (2008)
  • 6
    • 79953022502 scopus 로고    scopus 로고
    • Transformer fault diagnosis based on selective Bayes classifier
    • ZHAO Wenqing. Transformer fault diagnosis based on selective Bayes classifier[J]. Electric Power Automation Equipment, 2011, 31(2): 44-47.
    • (2011) Electric Power Automation Equipment , vol.31 , Issue.2 , pp. 44-47
    • Zhao, W.1
  • 7
    • 34249966007 scopus 로고
    • The CN2 induction algorithm
    • Clark P, Niblett T. The CN2 induction algorithm[J]. Machine Learning, 1989, 3(4): 261-283.
    • (1989) Machine Learning , vol.3 , Issue.4 , pp. 261-283
    • Clark, P.1    Niblett, T.2
  • 13
    • 84875069653 scopus 로고    scopus 로고
    • Transformer fault combination diagnosis based on multi-bayes classifier
    • Baoding, China: North China Electric Power University
    • ZHANG Bin. Transformer fault combination diagnosis based on multi-bayes classifier[D]. Baoding, China: North China Electric Power University, 2008.
    • (2008)
    • Zhang, B.1
  • 14
    • 34248636293 scopus 로고    scopus 로고
    • Fast sparse approximation for least squares support vector machine
    • Jiao L H, Bo L F, Wang L. Fast sparse approximation for least squares support vector machine[J]. IEEE Transactions on Neural Networks, 2007, 18(3): 685-690.
    • (2007) IEEE Transactions on Neural Networks , vol.18 , Issue.3 , pp. 685-690
    • Jiao, L.H.1    Bo, L.F.2    Wang, L.3
  • 15
    • 84864528429 scopus 로고    scopus 로고
    • Study on transformers fault diagnosis
    • Nanjing, China: Nanjing University of Science and Technology
    • XU Yongjian. Study on transformers fault diagnosis[D]. Nanjing, China: Nanjing University of Science and Technology, 2010.
    • (2010)
    • Xu, Y.1
  • 17
    • 84875081146 scopus 로고    scopus 로고
    • Study for transformer fault diagnosis and forecasting based on data mining
    • Baoding, China: North China Electric Power University
    • ZHAO Wenqing. Study for transformer fault diagnosis and forecasting based on data mining[D]. Baoding, China: North China Electric Power University, 2009.
    • (2009)
    • Zhao, W.1
  • 18
    • 0038137475 scopus 로고    scopus 로고
    • A method of synthesis based on the grey cluster and fuzzy cluster about internal fault diagnosis of transformer
    • LI Jian, SUN Caixin, CHEN Weigen, et al. A method of synthesis based on the grey cluster and fuzzy cluster about internal fault diagnosis of transformer[J]. Proceedings of the CSEE, 2006, 23(2): 112-115.
    • (2006) Proceedings of the CSEE , vol.23 , Issue.2 , pp. 112-115
    • Li, J.1    Sun, C.2    Chen, W.3
  • 19
    • 62749094131 scopus 로고    scopus 로고
    • Research on an intellectual fault diagnosis system for power transformer based on dissolved gas analysis technology
    • Wuhan, China: Wuhan University
    • PENG Ningyun. Research on an intellectual fault diagnosis system for power transformer based on dissolved gas analysis technology[D]. Wuhan, China: Wuhan University, 2004.
    • (2004)
    • Peng, N.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.