scholarly journals Probabilistic power flow computation using nested point estimate method

2021 ◽  
Author(s):  
Qing Xiao ◽  
Lianghong Wu ◽  
Chaoyang Chen
Keyword(s):  
Power Flow ◽  
Point Estimate ◽  
Flow Computation ◽  
Point Estimate Method ◽  
Estimate Method ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document