DocumentCode :
3091546
Title :
Fast Deterministic Sampling for Mean and Covariance Estimation in Stochastic Load Flow
Author :
Liao, Huaiwei
Author_Institution :
Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA
fYear :
2007
fDate :
24-28 June 2007
Firstpage :
1
Lastpage :
6
Abstract :
This paper proposes a new method for stochastic load flow (SLF) by using deterministic sampling based on sigma-point selection. In stead of conducting time-consuming Monte Carlo simulation, the proposed method can efficiently estimate the mean and covariance of state variables and branch power flow by conducting only 2k+ chosen deterministic power flow for a power system with k normally distributed uncertain parameters. The benefits of the proposed method are: 1) it has accuracy at least to the second order of truncated Taylor series; 2) it needs no derivatives; 3) it is not limited to the size of uncertain parameters. The effectiveness of the proposed method is demonstrated in an example of IEEE 14-bus power system.
Keywords :
covariance analysis; load flow; power system planning; power system state estimation; series (mathematics); stochastic processes; Taylor series; covariance estimation; fast deterministic sampling; k-normal distributed uncertain parameters; mean estimation; power network; sigma-point selection; stochastic load flow; Equations; Load flow; Network topology; Power system modeling; Power system planning; Power system simulation; Random variables; Sampling methods; State estimation; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Engineering Society General Meeting, 2007. IEEE
Conference_Location :
Tampa, FL
ISSN :
1932-5517
Print_ISBN :
1-4244-1296-X
Electronic_ISBN :
1932-5517
Type :
conf
DOI :
10.1109/PES.2007.385436
Filename :
4275318
Link To Document :
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