A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions

•A novel MPPT method based on an improved squirrel search algorithm (ISSA) is proposed.•Performance of the ISSA is compared with that of SSA, particle swarm optimization (PSO), and genetic algorithm (GA).•Simulation results demonstrate the effectiveness and the high tracking speed of the ISSA.•The f...

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Bibliographic Details
Published in:Energy conversion and management Vol. 230; p. 113773
Main Authors: Fares, Dalila, Fathi, Mohamed, Shams, Immad, Mekhilef, Saad
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 15.02.2021
Elsevier Science Ltd
Subjects:
administrative management
Algorithms
Bio- inspired algorithm
energy conversion
Genetic algorithms
Global maximum power point
literature
Maximum power tracking
Oscillations
Partial shading
Particle swarm optimization
PV system
Search algorithms
shade
Shading
solar collectors
Squirrel search algorithm
Squirrels
system optimization
Global maximum power point
Partial shading
PV system
Bio- inspired algorithm
Squirrel search algorithm
ISSN:0196-8904, 1879-2227
Online Access:Get full text
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Summary:•A novel MPPT method based on an improved squirrel search algorithm (ISSA) is proposed.•Performance of the ISSA is compared with that of SSA, particle swarm optimization (PSO), and genetic algorithm (GA).•Simulation results demonstrate the effectiveness and the high tracking speed of the ISSA.•The feasibility of the proposed method is validated experimentally.•Average efficiency obtained is 99.48% with a tracking time of 0.66 s. The partial shading condition (PSC) makes it challenging for the PV system to harvest maximum power via maximum power point tracking (MPPT). Various MPPT algorithms based on bio-inspired optimization methods were proposed in the literature. The mechanism employed by these algorithms varies from one to another, making them perform differently when tracking the GMPP. This paper introduces a novel MPPT technique based on the improved squirrel search algorithm (ISSA). The performance of the proposed ISSA improved the tracking time by 50% in comparison with the conventional SSA algorithm. Similarly, the proposed method has also been compared with popular Genetic algorithm (GA), and particle swarm optimization (PSO). The results proved the ability of the proposed algorithm in tracking the GMPP with faster convergence and fewer power oscillations in comparison. The feasibility and effectiveness of the proposed ISSA based MPPT have been validated experimentally, and the results clearly demonstrate its capability in tracking the GMPP with an average efficiency of 99.48% and average tracking time of 0.66 s.
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ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2020.113773