Technology for Transformer Fault Diagnosis with Detection and Monitoring Data Fusion

Lin Niu; Gui Ming Wang; Jie Zhan; Jin Tao Cui; Jin Xin Huang; Wan Jun Sun

doi:10.4028/www.scientific.net/AMR.860-863.2046

Paper Titles

An Optimal Automatic Generation Control Strategy for Interconnected Power Grid with Wind Power
p.2017

An Communication and Battery Performance Evaluation of the Demand Response and Battery Storage Coordination System Potential for Providing Microgrid Tie-Line Smoothing Services
p.2023

Study on Detection Method of Electrical Shock Current Based on Sliding Window and Wavelet Transform
p.2035

Economic Load Dispatch Using Bacterial Foraging Optimization Algorithm Based on Evolution Strategies
p.2040

Technology for Transformer Fault Diagnosis with Detection and Monitoring Data Fusion
p.2046

Based on Delay Compensation System for Shunt Active Power Filter Research
p.2050

A Kind of Equipment Fault Indicator Recognition Method
p.2054

Research on Monitoring System of Hydropower Substation Based on RFID Technology
p.2058

Analysis on Sub-Synchronous Damping Characteristic of VSC-HVDC
p.2062

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 860-863Technology for Transformer Fault Diagnosis with...

Technology for Transformer Fault Diagnosis with Detection and Monitoring Data Fusion

Abstract:

Fault diagnosis is an effective means to assure the safe operation of power system. In this paper, a detection and monitoring of transformer fault diagnosis of multi-source data fusion technology is introduced. Based on the correlation distance, to calculate the relationship between detection and monitoring data and equipment failure, using the weight function to set fault indicators, status online monitoring data over the fault indicators, judging the equipment what kind of failure is happened, improving the precision of fault diagnosis.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 860-863)

Pages:

2046-2049

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.860-863.2046

Citation:

Cite this paper

Online since:

December 2013

Authors:

Lin Niu, Gui Ming Wang, Jie Zhan, Jin Tao Cui, Jin Xin Huang, Wan Jun Sun

Keywords:

Distance Correlation, Fault Diagnosis, Multi-Source Data

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] GUO Chuangxin．PENG Mingwei．UU Yi．A novel approach with information fusion of multi-data resources in fault diagnosis of power grid[J]．Proceedings of the CSEE，2009, 29(31)：1-7.

Google Scholar

[2] Hu wenping. new technology research for power equipment fault diagnosis based on intelligent information fusion[D]. Huazhong University of Science and Technology, (2005).

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