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JCR Impact Factor: 0.650
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

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


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Direct Yaw Control of Vehicle using State Dependent Riccati Equation with Integral Terms, SANDHU, F., SELAMAT, H., MAHALLEH, V. B. S.
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Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

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  4/2018 - 13

Impact of Photovoltaic Systems Placement, Sizing on Power Quality in Distribution Network

YMERI, A. See more information about YMERI, A. on SCOPUS See more information about YMERI, A. on IEEExplore See more information about YMERI, A. on Web of Science, MUJOVIC, S. See more information about MUJOVIC, S. on SCOPUS See more information about MUJOVIC, S. on SCOPUS See more information about MUJOVIC, S. on Web of Science
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Download PDF pdficon (1,378 KB) | Citation | Downloads: 283 | Views: 688

Author keywords
distributed power generation, genetic algorithms, photovoltaic systems, power quality, power systems

References keywords
distribution(18), distributed(17), generation(14), system(8), power(8), losses(8), placement(6), networks(6), energy(6), algorithm(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 107 - 112
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04013
Web of Science Accession Number: 000451843400013
SCOPUS ID: 85058779829

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The paper presents a Decision Tree Algorithm for impact of photovoltaic systems placement and sizing in order to improve power quality in distribution networks. The proposed approach is based primarily on ID3 and J48 Decision Tree Algorithms. With these algorithms, for different sizes of photovoltaic systems, can be defined the optimal location as well as minimal power losses of the distribution network. The simulations were carried out with real data obtained from the Kosovo Distribution Network and visualized with WEKA Toolbox. The obtained results compared with Genetic Algorithm MATLAB toolbox and DIGSILENT/Power factory software, prove that the Decision Tree Algorithm works well with an excellent and fast accuracy. The results from the application of the proposed method showed reduced power losses and optimal location in the distribution network confirmed method's validity. This approach can be used by engineers, electric utilities and distribution network operators for a quick decision with more efficient integration of new photovoltaic systems in the current distribution networks.

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

[1] N. Mithulananthan, Th. Oo, L. V. Phu, "Distributed generator placement in power distribution system using genetic algorithm to reduce losses", Thammasat Int. J. Sc. Tech, vol. 9, No. 3, pp. 55-62, July-September 2004.

[2] G. Naik, D. K. Khatod, M. P. Sharma, "Distributed generation impact on distribution networks: A review", International Journal of Electrical and Electronics Engineering (IJEEE), vol. 2, Iss-1, pp. 68-72, 2012.

[3] V. Vita, T. Alimardan, L. Ekonomou, "The impact of distributed generation in the distribution networks' voltage pro?le and energy losses", 2015 IEEE European Modelling Symposium, pp. 260-265, 6-8 October 2015.
[CrossRef] [Web of Science Times Cited 23]

[4] D. Singh, D. Singh, K. S. Verma, "Distributed generation planning strategy with load models in radial distribution system", International Journal of Computer and Electrical Engineering, vol. 1, No. 3, pp. 362-375, August 2009.

[5] G. Naik, D. K. Khatod, M. P. Sharma, "Planning and operation of distributed generation in distribution networks", International Journal of Emerging Technology and Advanced Engineering, (IJETAE), vol. 2, Issue 9, pp. 381-388, September 2012.

[6] P. S. Georgilakis, N. D. Hatziargyriou, "Optimal distributed generation placement in power distribution networks: Models, methods, and future research", IEEE Transactions on Power Systems, vol. 28, No.3,pp. 3420-3428, 2013.
[CrossRef] [Web of Science Times Cited 351]

[7] D. Q. Hung, N. Mithulananthan, "Multiple distributed generators placement in primary distribution networks for loss reduction," IEEE Transactions on Industrial Electronics, vol. 60, No.4, pp.1700-1708, Apr.2013.
[CrossRef] [Web of Science Times Cited 275]

[8] S. Thongsuk, A. Ngaopitakkul, "Impacts of electrical line losses comprising multi-distributed generation in distributed system", Energy and Power Engineering, pp. 1037-1042, 2013, 5.

[9] P. Chiradeja, A. Ngaopitakkul, "The impact of electrical power losses due to distributed generation integration to distribution system", International Conference on Electrical Machines and Systems, pp. 1330-1333, Oct. 26-29, 2013.

[10] M. A. Shereen. "Optimal allocation of DG units for radial distribution systems using genetic algorithm", International Journal of Engineering and Advanced Technology (IJEAT), vol. 1, Issue. 6, pp.175-179, August 2012.

[11] D. C. L. Tamayo, P. A. N. Burgos, J. M. L. Lezama, "Optimal location and sizing of distributed generation: A review of the state of the art" Revista Polit├ęcnica, vol. 11, No. 20, pp. 99-110, 2015.

[12] Sh. Du, Q. Shao, G. Wang, "Analysis of DG influences on system losses in distribution network", International Journal of Grid Distribution Computing, vol. 8, No.5, pp.141-152, 2015.
[CrossRef] [Web of Science Times Cited 2]

[13] R. Prenc, N. Bogunovic, A. Cuculic, "The effect of distributed generation type and location constraints on the solution of the allocation algorithm", International Review of Electrical Engineering (IREE), vol. 10, No.1, pp. 88-97, 2015.

[14] M. Vukobratovic, Z. Hederic, M. Hadziselimovic, "Optimal distributed generation placement in distribution network", Energy Conference (Energycon), IEEE International, pp. 1236-1243, May 13-16, 2014.

[15] A. K. Saonerkar, B. Y. Bagde, B. S. Umre, "DG placement in distribution network for power loss minimization using genetic algorithm", International Journal of Research in Engineering andApplied Science, (IJREAS), vol. 02, Issue 02, pp. 41-47, July 2014.

[16] A. Ymeri, S. Mujovic, "Optimal location and sizing of photovoltaic systems in order to reduce power losses and voltage drops in the distribution grid", International Review of Electrical Engineering (I.R.E.E.), vol.12. No.6, pp. 498-504, November - December 2017.

[17] V. Vita, "Development of a decision-making algorithm for the optimum size and placement of distributed generation units in distribution networks", Energies, 2017, 10, 2043, pp. 1-13, 2017.
[CrossRef] [Web of Science Times Cited 21]

[18] M. Y. Arjun, R. S. Babu, "Impact of distributed generation on three feeder radial distribution system", Int. Journal of Engineering Research and Applications, vol. 3, Issue 6, pp. 983-988, Nov-Dec 2013.

[19] R. Rajaram, K. S. Kumar, N. Rajasekar, "Power system reconfiguration in a radial distribution network for reducing losses and to improve voltage profile using modified plant growth simulation algorithm with distributed generation (DG)", Energy Reports 1 (2015), Elsevier, pp. 116-122, 2015.
[CrossRef] [Web of Science Times Cited 56]

[20] A. Ymeri, L. Dervishi, A. Qorolli, "Impacts of distributed generation in energy losses and voltage drop in 10 kV line in the distribution system", Energy Conference (Energycon), IEEE International, May 13-16, 2014.

References Weight

Web of Science® Citations for all references: 728 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 35 ACR
SCOPUS® Average Citations per reference: 0

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 2019-11-12 08:04 in 102 seconds.

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Faculty of Electrical Engineering and Computer Science
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