NDLI: Probabilistic load flow calculation based on sparse polynomial chaos expansion

Content Provider	IET Digital Library
Author	Sun, Xin Tu, Qingrui Chen, Jinfu Zhang, Chengwen Duan, Xianzhong
Abstract	A probabilistic load flow (PLF) method based on the sparse polynomial chaos expansion (PCE) is presented here. Previous studies have shown that the generalised polynomial chaos expansion (gPCE) is promising for estimating the probability statistics and distributions of load flow outputs. However, it suffers the problem of curse-of-dimensionality in high-dimensional applications. Here, the compressive sensing technique is applied into the gPCE-based scheme, from which the sparse PCE is built as the surrogate model to perform the PLF in an accurate and efficient manner. The dependence among random input variables is also taken into consideration by making use of the Copula theory. Consequently, the proposed method is able to handle the correlated uncertainties of high-dimensionality and alleviate the computational effort as of popular methods. Finally, the feasibility and the effectiveness of the proposed method are validated by the case studies of two standard test systems.
Starting Page	2735
Ending Page	2744
Page Count	10
ISSN	17518687
Volume Number	12
e-ISSN	17518695
Issue Number	Issue 11, Jun (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/12/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2017.0859
Journal	IET Generation, Transmission & Distribution
Publisher Date	2018-03-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Chaos Compressed Sensing Compressive Sensing Technique Copula Theory Curse-of-dimensionality Problem Generalised Polynomial Chaos Expansion GPCE-based Scheme Load Flow Load Flow Output Distribution PLF PLF Method Polynomial Power System Probabilistic Load Flow Calculation Probability Probability Statistics Estimation Sparse Polynomial Chaos Expansion Statistical Analysis Statistics
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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