LAPSE:2020.0500
Published Article
LAPSE:2020.0500
A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems
May 22, 2020
The conventional maximum power point tracking (MPPT) method fails in partially shaded conditions, because multiple peaks may appear on the power−voltage characteristic curve. The Pigeon-Inspired Optimization (PIO) algorithm is a new type of meta-heuristic algorithm. Aiming at this situation, this paper proposes a new type of algorithm that combines a new pigeon population algorithm named Parallel and Compact Pigeon-Inspired Optimization (PCPIO) with MPPT, which can solve the problem that MPPT cannot reach the near global maximum power point. This hybrid algorithm is fast, stable, and capable of globally optimizing the maximum power point tracking algorithm. Therefore, the purpose of this article is to study the performance of two optimization techniques. The two algorithms are Particle Swarm Algorithm (PSO) and improved pigeon algorithm. This paper first studies the mechanism of multi-peak output characteristics of photovoltaic arrays in complex environments, and then proposes a multi-peak MPPT algorithm based on a combination of an improved pigeon population algorithm and an incremental conductivity method. The improved pigeon algorithm is used to quickly locate near the maximum power point, and then the variable step size incremental method INC (incremental conductance) is used to accurately locate the maximum power point. A simulation was performed on Matlab/Simulink platform. The results prove that the method can achieve fast and accurate optimization under complex environmental conditions, effectively reduce power oscillations, enhance system stability, and achieve better control results.
Keywords
meta-heuristic algorithm, MPPT, Particle Swarm Algorithm, Pigeon-Inspired Optimization
Suggested Citation
Tian AQ, Chu SC, Pan JS, Liang Y. A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems. (2020). LAPSE:2020.0500
Author Affiliations
Tian AQ: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China [ORCID]
Chu SC: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; College of Science and Engineering, Flinders University, 1284 South Road, Clovelly Park SA 5042, Australia [ORCID]
Pan JS: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; Department of Intelligence Science and Technology, College of Informantion Science and Technology, Dalian Maritime University, Dalian 116026 [ORCID]
Liang Y: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Journal Name
Processes
Volume
8
Issue
3
Article Number
E356
Year
2020
Publication Date
2020-03-20
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8030356, Publication Type: Journal Article
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LAPSE:2020.0500
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doi:10.3390/pr8030356
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May 22, 2020
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May 22, 2020
 
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Original Submitter
Calvin Tsay
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