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JCR Impact Factor: 1.102
JCR 5-Year IF: 0.734
Issues per year: 4
Current issue: Feb 2021
Next issue: May 2021
Avg review time: 54 days


PUBLISHER

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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2020-Jun-29
Clarivate Analytics published the InCites Journal Citations Report for 2019. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.102 (1.023 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.734.

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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
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (896 KB) | Citation | Downloads: 158 | Views: 474

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.


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


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