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Volumn 110, Issue , 2014, Pages 154-162

Application of an ensemble neural network for classifying partial discharge patterns

Author keywords

Ensemble neural network; Partial discharge; Single neural network

Indexed keywords

CLASSIFICATION (OF INFORMATION); NEURAL NETWORK MODELS;

EID: 84894252201     PISSN: 03787796     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.epsr.2014.01.010     Document Type: Review
Times cited : (40)

References (30)
  • 1
    • 79955624056 scopus 로고
    • Working group report 21.03. Recognition of discharges
    • Cigre
    • CIGRE Working group report 21.03. Recognition of discharges Electra 11 1969 61 98
    • (1969) Electra , Issue.11 , pp. 61-98
  • 3
    • 0027808474 scopus 로고
    • Neural network as a tool for recognition of partial discharges
    • E. Gulski, and A. Krivda Neural network as a tool for recognition of partial discharges IEEE Transactions on Electrical Insulation 28 6 1993 984 1001
    • (1993) IEEE Transactions on Electrical Insulation , vol.28 , Issue.6 , pp. 984-1001
    • Gulski, E.1    Krivda, A.2
  • 6
    • 0029185063 scopus 로고
    • Neural network system using the multi-layer perceptron technique for the recognition of partial discharge pulse shapes due to cavities and electrical trees
    • A. Mazroua, R. Bartnikas, and M. Salama Neural network system using the multi-layer perceptron technique for the recognition of partial discharge pulse shapes due to cavities and electrical trees IEEE Transactions on Power Delivery 10 1 1995 92 96
    • (1995) IEEE Transactions on Power Delivery , vol.10 , Issue.1 , pp. 92-96
    • Mazroua, A.1    Bartnikas, R.2    Salama, M.3
  • 8
    • 0035483913 scopus 로고    scopus 로고
    • Detection and classification of partial discharge using a feature decomposition-based modular neural network
    • T. Hong, and M. Fang Detection and classification of partial discharge using a feature decomposition-based modular neural network IEEE Transactions on Instrumentation and Measurement 50 5 2001 1349 1354
    • (2001) IEEE Transactions on Instrumentation and Measurement , vol.50 , Issue.5 , pp. 1349-1354
    • Hong, T.1    Fang, M.2
  • 9
    • 33748073455 scopus 로고    scopus 로고
    • Adaptive resonance theory 2 - An unsupervised NN for PD pattern recognition and classification
    • B. Karthikeyan, S. Gopal, and S. Venkatesh Adaptive resonance theory 2 - an unsupervised NN for PD pattern recognition and classification Expert Systems with Applications 31 2 2006 345 350
    • (2006) Expert Systems with Applications , vol.31 , Issue.2 , pp. 345-350
    • Karthikeyan, B.1    Gopal, S.2    Venkatesh, S.3
  • 10
    • 0032271413 scopus 로고    scopus 로고
    • Using counter propagation neural networks for partial discharge diagnosis
    • B. Freisleben, M. Hoof, and R. Patsch Using counter propagation neural networks for partial discharge diagnosis Neural Computing and Applications 7 1998 318 333
    • (1998) Neural Computing and Applications , vol.7 , pp. 318-333
    • Freisleben, B.1    Hoof, M.2    Patsch, R.3
  • 11
    • 0027576990 scopus 로고
    • Use of hidden Markov models for partial discharge pattern classification
    • L. Satish Use of hidden Markov models for partial discharge pattern classification IEEE Transactions on Electrical Insulation 28 1993 172 182
    • (1993) IEEE Transactions on Electrical Insulation , vol.28 , pp. 172-182
    • Satish, L.1
  • 13
    • 78651500131 scopus 로고    scopus 로고
    • Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network
    • D. Evagorou, A. Kyprianou, and P. Lewin Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network IET Science Measurement and Technology 4 3 2010 177 192
    • (2010) IET Science Measurement and Technology , vol.4 , Issue.3 , pp. 177-192
    • Evagorou, D.1    Kyprianou, A.2    Lewin, P.3
  • 14
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • L. Brieman Bagging predictors Machine Learning 24 1996 123 124
    • (1996) Machine Learning , vol.24 , pp. 123-124
    • Brieman, L.1
  • 20
    • 0029277377 scopus 로고    scopus 로고
    • Partial discharge. XIX. Discharges in air. Part I: Physical mechanisms
    • N. Trinh Partial discharge. XIX. Discharges in air. Part I: Physical mechanisms IEEE Electrical Insulation Magazine 11 2 1999 23 29
    • (1999) IEEE Electrical Insulation Magazine , vol.11 , Issue.2 , pp. 23-29
    • Trinh, N.1
  • 22
    • 0025558687 scopus 로고
    • Computer aided analysis of discharge patterns
    • E. Gulski, and F. Kreuger Computer aided analysis of discharge patterns Journal of Applied Physics 23 1990 1569 1575
    • (1990) Journal of Applied Physics , vol.23 , pp. 1569-1575
    • Gulski, E.1    Kreuger, F.2
  • 23
    • 84894276245 scopus 로고
    • Determination of discharge sources by analysis of discharge quantities as a function of time
    • Baltimore, USA
    • E. Gulski, and F. Kreuger Determination of discharge sources by analysis of discharge quantities as a function of time IEEE International Symposium on Electrical Insulation Baltimore, USA 1992 397 400
    • (1992) IEEE International Symposium on Electrical Insulation , pp. 397-400
    • Gulski, E.1    Kreuger, F.2
  • 26
    • 84894256495 scopus 로고    scopus 로고
    • Dynamically weighted ensemble of neural network for classification
    • Anchorage, USA
    • D. Jiminez Dynamically weighted ensemble of neural network for classification World Congress on Computational Intelligence Anchorage, USA 1998 753 756
    • (1998) World Congress on Computational Intelligence , pp. 753-756
    • Jiminez, D.1
  • 28
    • 0034333684 scopus 로고    scopus 로고
    • Stability problems with the artificial neural networks and the ensemble solution
    • P. Cunningham, J. Carna, and S. Jacob Stability problems with the artificial neural networks and the ensemble solution Artificial Intelligence in Medicine 20 3 2000 217 225
    • (2000) Artificial Intelligence in Medicine , vol.20 , Issue.3 , pp. 217-225
    • Cunningham, P.1    Carna, J.2    Jacob, S.3
  • 29
    • 0000926506 scopus 로고
    • When networks disagree ensemble methods for hybrid neural networks
    • R.J. Mammone, Chapman Hall USA
    • M. Perrone, and L. Cooper When networks disagree ensemble methods for hybrid neural networks R.J. Mammone, Neural Networks for Speech and Image Processing 1993 Chapman Hall USA 126 142
    • (1993) Neural Networks for Speech and Image Processing , pp. 126-142
    • Perrone, M.1    Cooper, L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.