Using SF6 decomposed component analysis for the diagnosis of partial discharge severity initiated by free metal particle defect

Energies

Volumn 10, Issue 8, 2017, Pages

  Tang, Ju ^a   Yang, Xu ^a   Yang, Dong ^a   Yao, Qiang ^b   Miao, Yulong ^b   Zhang, Chaohai ^c   Zeng, Fuping ^a  

^a WUHAN UNIVERSITY   (China)

^b STATE GRID CHONGQING ELECTRIC POWER COMPANY   (China)

^c Wuhan NARI Co Ltd   (China)

Author keywords

Back propagation neural network; DC gas insulated equipment; Decomposed component analysis; Feature selection; Partial discharge severity; SF6; Support vector machine

Indexed keywords

BACKPROPAGATION ALGORITHMS; CARBON DIOXIDE; DEFECTS; FEATURE EXTRACTION; METAL ANALYSIS; NEURAL NETWORKS; PARTIAL DISCHARGES; SULFUR HEXAFLUORIDE; SUPPORT VECTOR MACHINES; TORSIONAL STRESS;

BACK PROPAGATION NEURAL NETWORKS; COMPONENT ANALYSIS; CONCENTRATION RATIO; DECOMPOSITION CHARACTERISTICS; FEATURE SELECTION ALGORITHM; GAS INSULATED EQUIPMENT; MINIMUM REDUNDANCY-MAXIMUM RELEVANCES; SUPPORT VECTOR MACHINE ALGORITHM;

SF6 INSULATION;

EID: 85034847546     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en10081119     Document Type: Article

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