Copula Based Dependent Discrete Convolution for Power System Uncertainty Analysis

Discrete convolution (DC) is a generally accepted approach for the probabilistic analysis such as reliability assessment and probabilistic load flow. However, it has a strong precondition that the stochastic variables being convolved must be independent, which may not be fully satisfied in all cases...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on power systems Vol. 31; no. 6; pp. 5204 - 5205
Main Authors Ning Zhang, Chongqing Kang, Singh, Chanan, Qing Xia
Format Journal Article
LanguageEnglish
Published New York IEEE 01.11.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Convolution
copula
dependent
Dependent variables
Mathematical model
Power system reliability
Probabilistic analysis
Probabilistic logic
Probabilistic methods
Probability theory
Reliability
Reliability analysis
Uncertainty
Wind farms
Wind power
Online AccessGet full text

Cover

More Information
Summary:Discrete convolution (DC) is a generally accepted approach for the probabilistic analysis such as reliability assessment and probabilistic load flow. However, it has a strong precondition that the stochastic variables being convolved must be independent, which may not be fully satisfied in all cases. Using copula functions, this letter derives the formulation of DC for dependent variables. The performance of the proposed dependent discrete convolution (DDC) is illustrated using reliability assessment involving wind power. The result shows that the DDC inherits the efficient and reliable performance of DC, indicating a promising potential for practical applications.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2016.2521328