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Energies 2015, 8(6), 5338-5360; doi:10.3390/en8065338

Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique

1
Department of Electrical Engineering, National Taiwan University of Science and Technology, EE-105-1 #No.43, Sec. 4, Keelung Rd., Da'an Dist., Taipei 10600, Taiwan
2
Electric Energy Technology Division Power Electronics Department, Industrial Technology Research Institute, Rm#839, Bldg. 51, No. 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu 31040, Taiwan
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Frede Blaabjerg
Received: 5 March 2015 / Revised: 5 May 2015 / Accepted: 26 May 2015 / Published: 4 June 2015
View Full-Text   |   Download PDF [2282 KB, uploaded 4 June 2015]   |  

Abstract

In this paper, an asymmetrical fuzzy-logic-control (FLC)-based maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is presented. Two membership function (MF) design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V) curve of solar cells under standard test conditions (STC). The second method uses the particle swarm optimization (PSO) technique to optimize the input MF setting values. Because the PSO approach must target and optimize a cost function, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs) is also proposed. According to the simulated and experimental results, the proposed asymmetrical FLC-based MPPT method has the highest fitness value, therefore, it can successfully address the tracking speed/tracking accuracy dilemma compared with the traditional perturb and observe (P&O) and symmetrical FLC-based MPPT algorithms. Compared to the conventional FLC-based MPPT method, the obtained optimal asymmetrical FLC-based MPPT can improve the transient time and the MPPT tracking accuracy by 25.8% and 0.98% under STC, respectively. View Full-Text
Keywords: fuzzy logic control; maximum power point tracking; particle swarm optimization fuzzy logic control; maximum power point tracking; particle swarm optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Cheng, P.-C.; Peng, B.-R.; Liu, Y.-H.; Cheng, Y.-S.; Huang, J.-W. Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique. Energies 2015, 8, 5338-5360.

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