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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  1/2020 - 7

An Improved Analytical Methodology for Joint Distribution in Probabilistic Load Flow

WANG, T. See more information about WANG, T. on SCOPUS See more information about WANG, T. on IEEExplore See more information about WANG, T. on Web of Science, XIANG, Y., LI, C., MI, D., WANG, Z.
 
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Download PDF pdficon (896 KB) | Citation | Downloads: 39 | Views: 73

Author keywords
gaussian mixture model, maximum likelihood estimation, genetic algorithm, density function, distribution

References keywords
power(27), probabilistic(19), flow(17), system(14), load(14), tpwrs(11), systems(8), method(7), wind(6), research(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-02-28
Volume 20, Issue 1, Year 2020, On page(s): 49 - 56
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.01007
Web of Science Accession Number: 000518392600007

Abstract
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This paper presents a novel analytical method based on improved Gaussian mixture model (GMM) to solve the probabilistic load flow problem. The proposed method accounts for the uncertainty introduced due to increasing percentages of renewable generation. First, the joint probability density function of several wind farms outputs is derived by using the improved GMM with the estimated parameters obtained by genetic algorithm (GA) in this paper, which could improve the accuracy of the probabilistic model. Next, the analytical expressions between the output power of wind farms and line power of power system are deduced by linearizing load flow equations. And, the joint probability density function and joint cumulative distribution function of line power are obtained from linear load equation and joint probability density function of wind output power. Finally, the proposed method, Monte Carlo simulation (MCS) and traditional GMM based methods are all tested on a modified IEEE 39-bus system and a modified IEEE 118-bus system with multiple wind farms, which demonstrates the feasibility of the proposed method.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [SCOPUS Times Cited 589]


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References Weight

Web of Science® Citations for all references: 3,767 TCR
SCOPUS® Citations for all references: 5,124 TCR

Web of Science® Average Citations per reference: 145 ACR
SCOPUS® Average Citations per reference: 197 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-03-27 20:05 in 177 seconds.




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