A Kind of Equipment Fault Indicator Recognition Method

Article Preview

Abstract:

To analysis the main factors that lead to device fail and identify the fault indicator , this paper proposes a method about correlation identification between the multi-factor and equipments failure. Firstly, this paper sets the related factors influencing the equipments failure as numerical value, based on distance correlation definition, calculate the correlation between each factors and equipments failure. So that determines the power of the main causes of equipments failure. At last, through 330 kV transformer fault case validates the effectiveness and unbiasedness of the proposed method.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 860-863)

Pages:

2054-2057

Citation:

Online since:

December 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] GUO Chuangxin, ZHU Chuanbai, CAO Yijia. State of arts of fault diagnosis of power systems[J]. Automation of Electric Power Systems, 2006, 30(8): 102—107.

Google Scholar

[2] CARDOSO GJ,ROLlM J G . Applications of neural-network modules to electric power system fault section estimation[J]. IEEE Tran-sactions on Power Delivery, 2004, 19(3): 1034—1041.

DOI: 10.1109/tpwrd.2004.829911

Google Scholar

[3] G.J. Székely, M.L. Rizzo, Brownian distance covariance, Ann. Appl. Stat. 3 (4) (2009) 1236-1265.

Google Scholar

[4] G.J. Székely, M.L. Rizzo, Rejoinder: Brownian distance covariance, Ann. Appl. Stat. 3 (4) (2009) 1303-1308.

DOI: 10.1214/09-aoas312rej

Google Scholar

[5] G.J. Székely, M.L. Rizzo, N.K. Bakirov, Measuring and testing independence by correlation of distances, Ann. Statist. 35 (6) (2007) 2769-2794.

DOI: 10.1214/009053607000000505

Google Scholar

[6] Information on http: /www-stat. stanford. edu/~tibs/reshef/comment. pdf.

Google Scholar