Copula Based Dependent Discrete Convolution for Power System Uncertainty Analysis Academic Article

abstract

• 1969-2012 IEEE. 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.

authors

• Singh, Chanan

published proceedings

• IEEE TRANSACTIONS ON POWER SYSTEMS

author list (cited authors)

• Zhang, N., Kang, C., Singh, C., & Xia, Q.

citation count

• 38

complete list of authors

• Zhang, Ning||Kang, Chongqing||Singh, Chanan||Xia, Qing

publication date

• November 2016

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE TRANSACTIONS ON POWER SYSTEMS  Journal

keywords

• Convolution
• Copula
• Dependent
• Reliability
• Wind Power

Digital Object Identifier (DOI)

• 10.1109/TPWRS.2016.2521328

start page

• 5204

end page

• 5205

volume

• 31

issue

• 6

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Ftpwrs.2016.2521328

user-defined tag

• 7 Affordable and Clean Energy