Intelligent approaches using support vector machine and extreme learning machine for transmission line protection

Neurocomputing

Volumn 73, Issue 10-12, 2010, Pages 2160-2167

  Malathi, V ^a   Marimuthu, N S ^b   Baskar, S ^c  

^a Raja College of Engineering and Technology   (India)

^b NATIONAL ENGINEERING COLLEGE   (India)

^c THIAGARAJAR COLLEGE OF ENGINEERING   (India)

Author keywords

Extreme learning machine; Fault classification; Fault location; Support vector machine; Transmission line; Wavelet transform

Indexed keywords

CLASSIFICATION ACCURACY; EXTREME LEARNING MACHINE; FAULT CLASSIFICATION; FAULT CURRENTS; FAULT DISTANCE; FAULT INCEPTION ANGLES; FAULT LOCATION; FAULT RESISTANCES; HALF CYCLE; LINE CURRENTS; MATLAB-SIMULINK; MAXIMUM ERROR; MEAN ERRORS; PHASE DETECTION; POWER LEVELS; SOURCE IMPEDANCE; SWITCHING TRANSIENT; TRANSMISSION LINE; TRANSMISSION LINE PROTECTION;

DISCRETE WAVELET TRANSFORMS; ELECTRIC LINES; ELECTRIC POWER SYSTEM PROTECTION; MATLAB; SUPPORT VECTOR MACHINES; TRANSMISSION LINE THEORY; VECTORS; WAVELET DECOMPOSITION;

FAULT DETECTION;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; ELECTRIC CURRENT; FEASIBILITY STUDY; FREQUENCY ANALYZER; IMPEDANCE; LEARNING ALGORITHM; PERFORMANCE MEASUREMENT SYSTEM; PRIORITY JOURNAL; SIMULATION; SUPPORT VECTOR MACHINE; SYSTEM ANALYSIS; SYSTEMATIC ERROR;

EID: 77952551629     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.02.001     Document Type: Article

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