Comparison of swarm intelligence based global maximum power point tracking methods for photovoltaic generation system

Kun Che Ho; Cheng Chung Lin; F. Selin Bagci; Shun Chung Wang; Yi Hua Liu; Yu Shan Cheng

Comparison of swarm intelligence based global maximum power point tracking methods for photovoltaic generation system

Kun Che Ho
, Cheng Chung Lin
, F. Selin Bagci
, Shun Chung Wang
, Yi Hua Liu
, Yu Shan Cheng

National Taiwan University of Science and Technology
Lunghwa University of Science and Technology
Tokyo Metropolitan University

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

3 Scopus citations

Abstract

In this study, three swarm intelligence (SI)-based maximum power point tracking (MPPT) techniques for photovoltaic generation systems (PGSs) operating under partially shaded conditions (PSCs) are investigated. The compared methods include particle swarm optimization (PSO), firefly algorithm (FA) and cuckoo search (CS). Simulated results for 252 different shading patterns will be provided and some conclusion will be made. This study presents reference information for scholars planning to carry out research in this field.

Original language	English
Title of host publication	ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9788957083130
State	Published - 05 2019
Externally published	Yes
Event	10th International Conference on Power Electronics - ECCE Asia, ICPE 2019 - ECCE Asia - Busan, Korea, Republic of Duration: 27 05 2019 → 30 05 2019

Publication series

Name	ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia

Conference

Conference	10th International Conference on Power Electronics - ECCE Asia, ICPE 2019 - ECCE Asia
Country/Territory	Korea, Republic of
City	Busan
Period	27/05/19 → 30/05/19

Bibliographical note

Publisher Copyright:
© 2019 The Korean Institute of Power Electronics (KIPE).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Maximum power point tracking
Partially shaded conditions
Photovoltaic generation systems

Cite this

Ho, K. C., Lin, C. C., Bagci, F. S., Wang, S. C., Liu, Y. H., & Cheng, Y. S. (2019). Comparison of swarm intelligence based global maximum power point tracking methods for photovoltaic generation system. In ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia Article 8796901 (ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia). Institute of Electrical and Electronics Engineers Inc..

Ho, Kun Che ; Lin, Cheng Chung ; Bagci, F. Selin et al. / Comparison of swarm intelligence based global maximum power point tracking methods for photovoltaic generation system. ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia. Institute of Electrical and Electronics Engineers Inc., 2019. (ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia).

@inproceedings{75b7ee0523ff461e9ad715cdcb56d3f8,

title = "Comparison of swarm intelligence based global maximum power point tracking methods for photovoltaic generation system",

abstract = "In this study, three swarm intelligence (SI)-based maximum power point tracking (MPPT) techniques for photovoltaic generation systems (PGSs) operating under partially shaded conditions (PSCs) are investigated. The compared methods include particle swarm optimization (PSO), firefly algorithm (FA) and cuckoo search (CS). Simulated results for 252 different shading patterns will be provided and some conclusion will be made. This study presents reference information for scholars planning to carry out research in this field.",

keywords = "Maximum power point tracking, Partially shaded conditions, Photovoltaic generation systems",

author = "Ho, \{Kun Che\} and Lin, \{Cheng Chung\} and Bagci, \{F. Selin\} and Wang, \{Shun Chung\} and Liu, \{Yi Hua\} and Cheng, \{Yu Shan\}",

note = "Publisher Copyright: {\textcopyright} 2019 The Korean Institute of Power Electronics (KIPE).; 10th International Conference on Power Electronics - ECCE Asia, ICPE 2019 - ECCE Asia ; Conference date: 27-05-2019 Through 30-05-2019",

year = "2019",

month = may,

language = "英语",

series = "ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia",

address = "美国",

}

Ho, KC, Lin, CC, Bagci, FS, Wang, SC, Liu, YH & Cheng, YS 2019, Comparison of swarm intelligence based global maximum power point tracking methods for photovoltaic generation system. in ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia., 8796901, ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia, Institute of Electrical and Electronics Engineers Inc., 10th International Conference on Power Electronics - ECCE Asia, ICPE 2019 - ECCE Asia, Busan, Korea, Republic of, 27/05/19.

Comparison of swarm intelligence based global maximum power point tracking methods for photovoltaic generation system. / Ho, Kun Che; Lin, Cheng Chung; Bagci, F. Selin et al.
ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia. Institute of Electrical and Electronics Engineers Inc., 2019. 8796901 (ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia).

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

TY - GEN

T1 - Comparison of swarm intelligence based global maximum power point tracking methods for photovoltaic generation system

AU - Ho, Kun Che

AU - Lin, Cheng Chung

AU - Bagci, F. Selin

AU - Wang, Shun Chung

AU - Liu, Yi Hua

AU - Cheng, Yu Shan

PY - 2019/5

Y1 - 2019/5

N2 - In this study, three swarm intelligence (SI)-based maximum power point tracking (MPPT) techniques for photovoltaic generation systems (PGSs) operating under partially shaded conditions (PSCs) are investigated. The compared methods include particle swarm optimization (PSO), firefly algorithm (FA) and cuckoo search (CS). Simulated results for 252 different shading patterns will be provided and some conclusion will be made. This study presents reference information for scholars planning to carry out research in this field.

AB - In this study, three swarm intelligence (SI)-based maximum power point tracking (MPPT) techniques for photovoltaic generation systems (PGSs) operating under partially shaded conditions (PSCs) are investigated. The compared methods include particle swarm optimization (PSO), firefly algorithm (FA) and cuckoo search (CS). Simulated results for 252 different shading patterns will be provided and some conclusion will be made. This study presents reference information for scholars planning to carry out research in this field.

KW - Maximum power point tracking

KW - Partially shaded conditions

KW - Photovoltaic generation systems

UR - https://www.scopus.com/pages/publications/85071632780

M3 - 会议稿件

AN - SCOPUS:85071632780

T3 - ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia

BT - ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th International Conference on Power Electronics - ECCE Asia, ICPE 2019 - ECCE Asia

Y2 - 27 May 2019 through 30 May 2019

ER -

Ho KC, Lin CC, Bagci FS, Wang SC, Liu YH, Cheng YS. Comparison of swarm intelligence based global maximum power point tracking methods for photovoltaic generation system. In ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia. Institute of Electrical and Electronics Engineers Inc. 2019. 8796901. (ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia).

Comparison of swarm intelligence based global maximum power point tracking methods for photovoltaic generation system

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

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Cite this