Determining optimal membership functions of a FLC-based MPPT Algorithm using the particle swarm optimization method

Yi Hua Liu; Shun Chung Wang; Bo Ruei Peng

doi:10.1109/IIAI-AAI.2016.123

Determining optimal membership functions of a FLC-based MPPT Algorithm using the particle swarm optimization method

Yi Hua Liu, Shun Chung Wang, Bo Ruei Peng

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Fuzzy-logic-control (FLC)-based maximum power point tracking (MPPT) algorithm can successfully deal with the transient time/tracking accuracy dilemma of the commonly utilized perturb and observe (P&O) method, however, optimal setting of the membership functions (MFs) is hard to find. In this paper, particle swarm optimization (PSO) technique is adopted to determine the optimal input MF setting values. According to the simulated and experimental results, the obtained optimal input MF values can improve the averaged MPPT tracking accuracy by 1.31 %. Moreover, the averaged fitness value can significantly be improved by 25.6 %.

Original language	English
Title of host publication	Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
Editors	Ayako Hiramatsu, Tokuro Matsuo, Akimitsu Kanzaki, Norihisa Komoda
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	635-640
Number of pages	6
ISBN (Electronic)	9781467389853
DOIs	https://doi.org/10.1109/IIAI-AAI.2016.123
State	Published - 31 08 2016
Externally published	Yes
Event	5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, Japan Duration: 10 07 2016 → 14 07 2016

Publication series

Name	Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016

Conference

Conference	5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
Country/Territory	Japan
City	Kumamoto
Period	10/07/16 → 14/07/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Fuzzy logic control
Maximum power point tracking
Particle swarm optimization

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10.1109/IIAI-AAI.2016.123

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Liu, Y. H., Wang, S. C., & Peng, B. R. (2016). Determining optimal membership functions of a FLC-based MPPT Algorithm using the particle swarm optimization method. In A. Hiramatsu, T. Matsuo, A. Kanzaki, & N. Komoda (Eds.), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 (pp. 635-640). Article 7557690 (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI.2016.123

Liu, Yi Hua ; Wang, Shun Chung ; Peng, Bo Ruei. / Determining optimal membership functions of a FLC-based MPPT Algorithm using the particle swarm optimization method. Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. editor / Ayako Hiramatsu ; Tokuro Matsuo ; Akimitsu Kanzaki ; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 635-640 (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016).

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abstract = "Fuzzy-logic-control (FLC)-based maximum power point tracking (MPPT) algorithm can successfully deal with the transient time/tracking accuracy dilemma of the commonly utilized perturb and observe (P&O) method, however, optimal setting of the membership functions (MFs) is hard to find. In this paper, particle swarm optimization (PSO) technique is adopted to determine the optimal input MF setting values. According to the simulated and experimental results, the obtained optimal input MF values can improve the averaged MPPT tracking accuracy by 1.31 %. Moreover, the averaged fitness value can significantly be improved by 25.6 %.",

keywords = "Fuzzy logic control, Maximum power point tracking, Particle swarm optimization",

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year = "2016",

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doi = "10.1109/IIAI-AAI.2016.123",

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Liu, YH, Wang, SC & Peng, BR 2016, Determining optimal membership functions of a FLC-based MPPT Algorithm using the particle swarm optimization method. in A Hiramatsu, T Matsuo, A Kanzaki & N Komoda (eds), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016., 7557690, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Institute of Electrical and Electronics Engineers Inc., pp. 635-640, 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Kumamoto, Japan, 10/07/16. https://doi.org/10.1109/IIAI-AAI.2016.123

Determining optimal membership functions of a FLC-based MPPT Algorithm using the particle swarm optimization method. / Liu, Yi Hua; Wang, Shun Chung; Peng, Bo Ruei.
Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. ed. / Ayako Hiramatsu; Tokuro Matsuo; Akimitsu Kanzaki; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. p. 635-640 7557690 (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Fuzzy-logic-control (FLC)-based maximum power point tracking (MPPT) algorithm can successfully deal with the transient time/tracking accuracy dilemma of the commonly utilized perturb and observe (P&O) method, however, optimal setting of the membership functions (MFs) is hard to find. In this paper, particle swarm optimization (PSO) technique is adopted to determine the optimal input MF setting values. According to the simulated and experimental results, the obtained optimal input MF values can improve the averaged MPPT tracking accuracy by 1.31 %. Moreover, the averaged fitness value can significantly be improved by 25.6 %.

AB - Fuzzy-logic-control (FLC)-based maximum power point tracking (MPPT) algorithm can successfully deal with the transient time/tracking accuracy dilemma of the commonly utilized perturb and observe (P&O) method, however, optimal setting of the membership functions (MFs) is hard to find. In this paper, particle swarm optimization (PSO) technique is adopted to determine the optimal input MF setting values. According to the simulated and experimental results, the obtained optimal input MF values can improve the averaged MPPT tracking accuracy by 1.31 %. Moreover, the averaged fitness value can significantly be improved by 25.6 %.

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Liu YH, Wang SC, Peng BR. Determining optimal membership functions of a FLC-based MPPT Algorithm using the particle swarm optimization method. In Hiramatsu A, Matsuo T, Kanzaki A, Komoda N, editors, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 635-640. 7557690. (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016). doi: 10.1109/IIAI-AAI.2016.123

Determining optimal membership functions of a FLC-based MPPT Algorithm using the particle swarm optimization method

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Keywords

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