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Volumn 76, Issue 12, 2006, Pages 1047-1054

A feed-forward artificial neural network with enhanced feature selection for power system transient stability assessment

Author keywords

Feature selection; Neural networks; Transient stability analysis

Indexed keywords

COMPUTER AIDED SOFTWARE ENGINEERING; FEEDFORWARD NEURAL NETWORKS; ONLINE SYSTEMS; SECURITY SYSTEMS; TIME DOMAIN ANALYSIS;

EID: 33745194150     PISSN: 03787796     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.epsr.2005.12.026     Document Type: Article
Times cited : (73)

References (15)
  • 4
    • 4043101022 scopus 로고    scopus 로고
    • IEEE/CIGRE Joint Task Force, Definition and classification of power system stability, IEEE Trans. Power Syst. 19 (2) (2004).
  • 5
    • 33745195855 scopus 로고    scopus 로고
    • P. Kundur, in: Power System Stability and Control, McGraw-Hill, 1994.
  • 7
    • 0030378702 scopus 로고    scopus 로고
    • M.A.El-Sharkawi, Neural Networks Power, IEEE Potentials, 15 (5) (1996-1997) 12-15.
  • 10
    • 33745223350 scopus 로고    scopus 로고
    • TSAT Version 3.0, No. 3, Powertech Labs, BC, Canada, 2003.
  • 11
    • 33745223716 scopus 로고    scopus 로고
    • IEEE Committee Report, Techniques for power system stability limit search, Catalog Number, TP-138-0.
  • 12
    • 0024611597 scopus 로고
    • Artificial neural net based dynamic security assessment for electric power systems
    • Sobajic D.J., and Yon-Han Pao. Artificial neural net based dynamic security assessment for electric power systems. IEEE Trans. Power Syst. 14 1 (1989) 954-960
    • (1989) IEEE Trans. Power Syst. , vol.14 , Issue.1 , pp. 954-960
    • Sobajic, D.J.1    Yon-Han Pao2
  • 14
    • 33745224823 scopus 로고    scopus 로고
    • MATLAB Version 6.5.0, Math Works Inc., 2002.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.