Bayesian fault detection based on WAMS/PMU measurement system

Author

Yagang Zhang ; Zengping Wang ; Jinfang Zhang

Author_Institution

State Key Lab. of Alternate Electr. Power Syst. with Renewable Energy Sources, North China Electr. Power Univ., Baoding, China

fYear

2012

fDate

22-26 July 2012

Firstpage

1

Lastpage

6

Abstract

The Bayesian fault detection based on WAMS/PMU measurement system will be clarified in this paper. Multivariate statistical analysis is an effective tool to finish the fault detection for electric power system. In Bayesian discriminant analysis as a sub branch, by the research of several populations, one can calculate the conditional probability that some samples belong to these populations, and compare the corresponding probability. The sample will be classified as population with maximum probability. A great number of simulation examples have confirmed that the results of Bayesian fault detection in wide area backup protection are accurate and reliable.

Keywords

Bayes methods; fault diagnosis; phasor measurement; power system protection; probability; Bayesian discriminant analysis; Bayesian fault detection; WAMS-PMU measurement system; conditional probability; electric power system; multivariate statistical analysis; wide area backup protection; Bayesian methods; Electrical fault detection; Fault detection; Phasor measurement units; Power systems; Sociology; Statistics; Bayesian discriminant analysis; PMU; WAMS; fault detection; posterior probability;

fLanguage

English

Publisher

ieee

Conference_Titel

Power and Energy Society General Meeting, 2012 IEEE

Conference_Location

San Diego, CA

ISSN

1944-9925

Print_ISBN

978-1-4673-2727-5

Electronic_ISBN

1944-9925

Type

conf

DOI

10.1109/PESGM.2012.6345199

Filename

6345199