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Volumn 28, Issue 14, 2004, Pages 63-66

Application of RPROP ANN based fault diagnosis model for transformer dissolved gas-in-oil analysis

Author keywords

Condition based maintenance; Dissolved gas in oil analysis (DGA); Fault diagnosis; Main transformer; Neural network; Resilient propagation (RPROP); Transformation equipment

Indexed keywords

BACKPROPAGATION; ELECTRIC EQUIPMENT; FAILURE ANALYSIS; NEURAL NETWORKS;

EID: 4644246016     PISSN: 10001026     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (11)

References (14)
  • 1
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    • (1987)
  • 2
    • 4644287313 scopus 로고    scopus 로고
    • Overview of analysis and diagnosis of the dissolved gas in transformer oil
    • Qian Zhiyin, Lu Zhihao, Lou Qimin, et al. Overview of Analysis and Diagnosis of the Dissolved Gas in Transformer Oil. High Voltage Engineering, 2002, 38(6): 34-37
    • (2002) High Voltage Engineering , vol.38 , Issue.6 , pp. 34-37
    • Qian, Z.1    Lu, Z.2    Lou, Q.3
  • 3
    • 0037735095 scopus 로고    scopus 로고
    • Fault diagnosis of power transformer using a combinatorial neural network
    • Liu Na, Gao Wensheng, Tan Kexiong. Fault Diagnosis of Power Transformer Using a Combinatorial Neural Network. Transactions of China Electro Technical Society. 2003, 18(2): 83-86
    • (2003) Transactions of China Electro Technical Society , vol.18 , Issue.2 , pp. 83-86
    • Liu, N.1    Gao, W.2    Tan, K.3
  • 4
    • 4644317818 scopus 로고    scopus 로고
    • The application of BP for dissolved gas analysis based on transformer fault diagnosis
    • Huang Juming, Zhu Zishu, Hu Wenhua, et al. The Application of BP for Dissolved Gas Analysis Based on Transformer Fault Diagnosis. High Voltage Engineering, 1996, 22(2): 21-23
    • (1996) High Voltage Engineering , vol.22 , Issue.2 , pp. 21-23
    • Huang, J.1    Zhu, Z.2    Hu, W.3
  • 5
    • 0001278980 scopus 로고
    • A self-optimizing, nonsymmetrical neural net for content addressable memory and pattern recognition
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    • Lapedes, A.1    Farber, R.2
  • 6
    • 34250094997 scopus 로고
    • Accelerating the convergence of the back-propagation method
    • Vogl T P, Mangis J K, Rigler A K, et al. Accelerating the Convergence of the Back-propagation Method. Biological Cybernetics, 1988, 59: 257-263
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    • Vogl, T.P.1    Mangis, J.K.2    Rigler, A.K.3
  • 7
    • 0001209372 scopus 로고
    • Accelerated back-propagation learning: Two optimization methods
    • Roberto Battiti. Accelerated Back-propagation Learning: Two Optimization Methods. Complex Systems, 1989, 3: 331-342
    • (1989) Complex Systems , vol.3 , pp. 331-342
    • Battiti, R.1
  • 8
    • 0025593679 scopus 로고
    • SuperSAB: Fast adaptive back-propagation with good scaling properties
    • Tollenaere T. SuperSAB: Fast Adaptive Back-propagation with Good Scaling Properties. Neural Networks, 1990, 3(5): 561-573
    • (1990) Neural Networks , vol.3 , Issue.5 , pp. 561-573
    • Tollenaere, T.1
  • 9
    • 84976832431 scopus 로고
    • Methods to speed up error back-propagation learning algorithm
    • Dilip Sarkar. Methods to Speed up Error Back-propagation Learning Algorithm. ACM Computing Surveys, 1995, 27(4): 519-542
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  • 13
    • 0042317297 scopus 로고    scopus 로고
    • On-line data rectification for chemical process measurements based on artificial neural networks
    • Pan Jizheng, Zhou Chuanguang, Qian Yu. On-line Data Rectification for Chemical Process Measurements Based on Artificial Neural Networks. Journal of Chemical Engineering of Chinese Universities, 2003, 17(3): 319-324
    • (2003) Journal of Chemical Engineering of Chinese Universities , vol.17 , Issue.3 , pp. 319-324
    • Pan, J.1    Zhou, C.2    Qian, Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.