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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

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


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Improved Wind Speed Prediction Using Empirical Mode Decomposition, ZHANG, Y., ZHANG, C., SUN, J., GUO, J.
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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. See more information about  XIANG, Y. on SCOPUS See more information about  XIANG, Y. on SCOPUS See more information about XIANG, Y. on Web of Science, LI, C. See more information about  LI, C. on SCOPUS See more information about  LI, C. on SCOPUS See more information about LI, C. on Web of Science, MI, D. See more information about  MI, D. on SCOPUS See more information about  MI, D. on SCOPUS See more information about MI, D. on Web of Science, WANG, Z. See more information about WANG, Z. on SCOPUS See more information about WANG, Z. on SCOPUS See more information about WANG, Z. on Web of Science
 
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Download PDF pdficon (896 KB) | Citation | Downloads: 132 | Views: 347

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
SCOPUS ID: 85083745612

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 622]


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References Weight

Web of Science® Citations for all references: 4,272 TCR
SCOPUS® Citations for all references: 5,748 TCR

Web of Science® Average Citations per reference: 164 ACR
SCOPUS® Average Citations per reference: 221 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 2020-11-25 20:02 in 174 seconds.




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