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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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  4/2017 - 9

 HIGHLY CITED PAPER 

Genetically Optimization of an Asymmetrical Fuzzy Logic Based Photovoltaic Maximum Power Point Tracking Controller

AL-GIZI, A. See more information about AL-GIZI, A. on SCOPUS See more information about AL-GIZI, A. on IEEExplore See more information about AL-GIZI, A. on Web of Science, AL-CHLAIHAWI, S. See more information about  AL-CHLAIHAWI, S. on SCOPUS See more information about  AL-CHLAIHAWI, S. on SCOPUS See more information about AL-CHLAIHAWI, S. on Web of Science, LOUZAZNI, M. See more information about  LOUZAZNI, M. on SCOPUS See more information about  LOUZAZNI, M. on SCOPUS See more information about LOUZAZNI, M. on Web of Science, CRACIUNESCU, A. See more information about CRACIUNESCU, A. on SCOPUS See more information about CRACIUNESCU, A. on SCOPUS See more information about CRACIUNESCU, A. 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 (1,592 KB) | Citation | Downloads: 490 | Views: 1,737

Author keywords
fuzzy logic, genetic algorithms, maximum power point trackers, optimization, photovoltaic systems

References keywords
fuzzy(12), tracking(10), logic(10), power(9), point(9), maximum(8), system(7), photovoltaic(7), algorithms(6), systems(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-11-30
Volume 17, Issue 4, Year 2017, On page(s): 69 - 76
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.04009
Web of Science Accession Number: 000417674300009
SCOPUS ID: 85035755676

Abstract
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Full text preview
This paper introduces a new fuzzy logic controller (FLC) based photovoltaic (PV) maximum power point tracking (MPPT) optimized with the genetic algorithm (GA). Four FLCs with five and seven numbers of triangular (tri) and generalized bell (g-bell) membership functions (MFs) are analyzed. The performances of the analyzed algorithms have been compared with the appropriate performances of the classical perturb and observe (P&O) algorithm by using the following criteria: the rise time (tr), the tracking accuracy of the output power, and the energy yield. The results showed that the FL-based PV MPPT controller with seven triangular (7-tri) MFs provides the best steady-state performances.


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

[1] S. Saravanan, R. Babu, "Maximum power point tracking algorithms for photovoltaic system- a review," Renewable and Sustainable Energy Reviews, vol. 57, pp. 192-204, 2016.
[CrossRef] [Web of Science Times Cited 139] [SCOPUS Times Cited 179]


[2] T. M. Mohan, V. Vakula, "Comparative analysis of perturb & observe and fuzzy logic maximum power point tracking techniques for a photovoltaic array under partial shading conditions," Leonardo Journal of Sciences, vol. 27, pp. 1-16, Jul. 2015.

[3] A. G. Al-Gizi and S. J. Al-Chlaihawi, "Study of FLC based MPPT in comparison with P&O and InC for PV systems," in Proc. of IEEE International Symposium on Fundamentals of Electrical Engineering (ISFEE 2016), Bucharest, Romania, 2016, pp. 1-6.
[CrossRef] [SCOPUS Times Cited 16]


[4] A. G. Al-Gizi, "Comparative study of MPPT algorithms under variable resistive load," in Proc. of IEEE International Conference on Applied and Theoretical Electricity (ICATE 2016), Craiova, Romania, 2016, pp. 1-6.
[CrossRef] [SCOPUS Times Cited 16]


[5] A. M. Othman, M. M. El-Arini, A. Ghitas, A. Fathy, "Realworld maximum power point tracking simulation of PV system based on fuzzy logic control," NRIAG Journal of Astronomy and Geophysics, vol. 1, no. 2, pp. 186-194, 2012.
[CrossRef]


[6] F. L. Tofoli, D. de Castro Pereira, W. J. de Paula, "Comparative study of maximum power point tracking techniques for photovoltaic systems," International Journal of Photoenergy, vol. 2015, pp. 1-10, Jan. 2015.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 35]


[7] C. L. Liu, J. H. Chen, Y. H. Liu, Z. Z. Yang, "An asymmetrical fuzzy-logic-control-based MPPT algorithm for photovoltaic systems," Energies, vol. 7, no. 4, pp. 2177-2193, Apr. 2014.
[CrossRef] [Web of Science Times Cited 51] [SCOPUS Times Cited 57]


[8] P. C. Cheng, B. R. Peng, Y. H. Liu, Y. S. Cheng, J. W. Huang, "Optimization of a fuzzy-logic-control-based MPPT algorithm using the particle swarm optimization technique," Energies, vol. 8, no. 6, pp. 5338-5360, 2015.
[CrossRef] [Web of Science Times Cited 58] [SCOPUS Times Cited 72]


[9] N. Altin, "Interval type-2 fuzzy logic controller based maximum power point tracking in photovoltaic systems," Advances in Electrical and Computer Engineering, vol. 13, no. 3, pp. 65-70, 2013.
[CrossRef] [Full Text] [Web of Science Times Cited 23] [SCOPUS Times Cited 28]


[10] A. Durusu, I. Nakir, A. Ajder, R. Ayaz, H. Akca, M. Tanrioven, "Performance comparison of widely used maximum power point tracking algorithms under real environmental conditions," Advances in Electrical and Computer Engineering, vol. 14, no. 3, pp. 89-94, 2014.
[CrossRef] [Full Text] [Web of Science Times Cited 7] [SCOPUS Times Cited 8]


[11] D. Petreus, D. Moga, A. Rusu, T. Patarau, M. Munteanu, "Photovoltaic system with smart tracking of the optimal working point," Advances in Electrical and Computer Engineering, vol. 10, no. 3, pp. 40-47, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 7] [SCOPUS Times Cited 9]


[12] A. Rahma and M. Khemliche, "Combined approach between FLC and PSO to find the best MFs to improve the performance of PV system under variable climate conditions and load requirements," in Proc. of IEEE International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM 2014), Nov. 3-6, 2014, pp. 1-8.
[CrossRef] [SCOPUS Times Cited 2]


[13] N. Hashim, Z. Salam, and S. M. Ayob, "Maximum power point tracking for stand-alone photovoltaic system using evolutionary programming," in Proc. of IEEE 8th International Power Engineering and Optimization Conference (PEOCO2014), Langkawi, Malaysia, 2014, pp. 7-12.
[CrossRef] [SCOPUS Times Cited 15]


[14] A. Messai, A. Mellit, A. Guessoum, S. A. Kalogirou, "Maximum power point tracking using a GA optimized fuzzy logic controller and its FPGA implementation," Solar Energy, vol. 85, no. 2, pp. 265-277, 2011.

[15] M. T. Guneser, E. Erdil, M. Cernat, T. Ozturk, "Improving the energy management of a solar electric vehicle," Advances in Electrical and Computer Engineering, vol. 15, no. 4, pp. 53-62, 2015.
[CrossRef] [Full Text] [Web of Science Times Cited 3] [SCOPUS Times Cited 4]


[16] E. Sahin, I. H. Altas, "FPA tuned fuzzy logic controlled synchronous buck converter for a wave/sc energy system," Advances in Electrical and Computer Engineering, vol. 17, no. 1, pp. 39-48, 2017.
[CrossRef] [Full Text] [Web of Science Times Cited 4] [SCOPUS Times Cited 5]


[17] A. G. Al-Gizi, "Multi-stages for tuning of fuzzy logic controller (FLC) using genetic algorithm (GA)," Eng. & Tech. Journal, vol. 31, no. 6, part A, pp. 1166-1181, 2013.

[18] A. G. Al-Gizi, E. A. Hussein, "FPGA-based implementation of genetically tuned fuzzy logic controller (GA-FLC)," Journal of Engineering and Development, vol. 16, no. 3, pp. 241-257, Sep. 2012.

[19] S. A. Al-Obaidi, M. N. Al-Tikriti, A. G. Al-Gizi, "Tuning of composite fuzzy logic guidance law using genetic algorithms," Eng. & Tech. Journal, vol. 30, no. 13, pp. 2341-2356, Jan. 2012.

[20] F. Herrera, L. Magdalena, "Genetic fuzzy systems: a tutorial," Tatra Mountains Mathematical Publications, vol. 13, pp. 93-121, Jun. 1997.

[21] P. Wang, D. P. Kwok, "Optimal design of PID process controllers based on genetic algorithms," Control Eng. Pract., vol. 2, no. 4, pp. 641-648, 1994.

[22] J. H. Van der Lee, W. Y. Svrcek, B. R. Young, "A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making," ISA Trans., vol. 47, no. 1, pp. 53-59, 2008.

[23] B. Sarkar, P. Mandal, R. Saha, S. Mookherjee, D. Sanyal, "GA-optimized feed forward-PID tracking control for a rugged electrohydraulic system design," ISA Trans., vol. 52, no. 6, pp. 853-861, 2013.

[24] Matlab, Global Optimization Toolbox, "User's Guide (R2015b)," The MathWorks Inc., 2015.



References Weight

Web of Science® Citations for all references: 316 TCR
SCOPUS® Citations for all references: 446 TCR

Web of Science® Average Citations per reference: 13 ACR
SCOPUS® Average Citations per reference: 18 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 2021-03-04 07:49 in 89 seconds.




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