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access icon free Point estimate method based on univariate dimension reduction model for probabilistic power flow computation

  • Author(s): Qing Xiao 1, 2 ; Ying He 2 ; Kuineng Chen 2 ; Yang Yang 2 ; Yonghui Lu 2
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  • Source: Volume 11, Issue 14, 28 September 2017, p. 3522 – 3531
    DOI:  10.1049/iet-gtd.2017.0023 , Print ISSN 1751-8687, Online ISSN 1751-8695
© The Institution of Engineering and Technology
Received 05/01/2017, Accepted 21/06/2017, Revised 24/04/2017, Published 22/06/2017

This study employs Gaussian copula to model correlated non-normal variables in power system, whereby the probabilistic power flow (PPF) problem is transformed to independent standard normal space. In conjunction with univariate dimension reduction model, two quadrature rules: Gauss-logistic (GL) quadrature and Clenshaw–Curtis (CC) quadrature, are developed to calculate the moments of PPF solutions; for CC quadrature, the weights and nodes are given explicitly. Testing on a modified 118-bus system, it is found that CC quadrature converges more uniformly than the generally used Gauss–Hermite quadrature, and GL quadrature is more accurate.

Inspec keywords: probability; load flow

Other keywords: power system; PPF problem; Gauss-Hermite quadrature; Gaussian copula; modified 118-bus system; quadrature rules; correlated nonnormal variables; univariate dimension reduction model; point estimate method; Clenshaw-Curtis quadrature; probabilistic power flow computation; Gauss-logistic quadrature

Subjects: Power systems; Other topics in statistics

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