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

2017 ◽  
Vol 11 (14) ◽  
pp. 3522-3531 ◽  
Author(s):  
Qing Xiao ◽  
Ying He ◽  
Kuineng Chen ◽  
Yang Yang ◽  
Yonghui Lu
Keyword(s):  
Dimension Reduction ◽  
Power Flow ◽  
Point Estimate ◽  
Reduction Model ◽  
Flow Computation ◽  
Point Estimate Method ◽  
Estimate Method ◽  
Probabilistic Power Flow

Investigating Univariate Dimension Reduction Model for Probabilistic Power Flow Computation

2019 ◽  
Vol 47 (6-7) ◽  
pp. 561-572 ◽  
Author(s):  
Qing Xiao ◽  
Shaowu Zhou ◽  
Lianghong Wu ◽  
Yanming Zhao ◽  
You Zhou
Keyword(s):  
Dimension Reduction ◽  
Power Flow ◽  
Reduction Model ◽  
Flow Computation ◽  
Probabilistic Power Flow

Comparing unscented transformation and point estimate method for probabilistic power flow computation

2018 ◽  
Vol 37 (3) ◽  
pp. 1290-1303 ◽  
Author(s):  
Qing Xiao ◽  
Shaowu Zhou
Keyword(s):  
Power Flow ◽  
Quadrature Rule ◽  
Point Estimate ◽  
Unscented Transformation ◽  
Content Type ◽  
Point Estimate Method ◽  
Estimate Method ◽  
Gauss Type ◽  
Probability Spaces ◽  
Probabilistic Power Flow

Purpose Unscented transformation (UT) and point estimate method (PEM) are two efficient algorithms for probabilistic power flow (PPF) computation. This paper aims to show the relevance between UT and PEM and to derive a rule to determine the accuracy controlling parameters for UT method. Design/methodology/approach The authors derive the underlying sampling strategies of UT and PEM and check them in different probability spaces, where quadrature nodes are selected. Findings Gauss-type quadrature rule can be used to determine the accuracy controlling parameters of UT. If UT method and PEM select quadrature nodes in two probability spaces related by a linear transform, these two algorithms are equivalent. Originality/value It shows that UT method can be conveniently extended to (km + 1) scheme (k = 4; 6; : : : ) by Gauss-type quadrature rule.

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