Publication:
Comparative analysis of two-step GA-based PV array reconfiguration technique and other reconfiguration techniques

Simple item page
dc.citedby41
dc.contributor.authorMuhammad Ajmal A.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorNaderipour A.en_US
dc.contributor.authorEkanayake J.B.en_US
dc.contributor.authorid57217176335en_US
dc.contributor.authorid6602912020en_US
dc.contributor.authorid36677578000en_US
dc.contributor.authorid7003409510en_US
dc.date.accessioned2023-05-29T09:09:02Z
dc.date.available2023-05-29T09:09:02Z
dc.date.issued2021
dc.descriptionDynamic models; Electric connectors; MATLAB; Particle swarm optimization (PSO); Photovoltaic cells; Software testing; Solar power generation; Comparative analysis; Comparative studies; Dynamic reconfiguration techniques; Dynamic techniques; Electrical connection; Matlab/Simulink software; Performance analysis; Real-time application; Genetic algorithmsen_US
dc.description.abstractPhotovoltaic (PV) plants can be exposed to partial shading, which reduces the energy production and causes multi-peaks to form in the Power-Voltage (P-V) curve. As a result, the row currents of the PV modules will not be constant. Several techniques have been proposed to overcome partial shading, such as the static and dynamic reconfiguration techniques, with both aiming to reduce the difference in the row currents to improve energy production. Minimization of the row current via static techniques requires laborious work and extra wiring. On the other hand, dynamic techniques require an extensive monitoring system to support different tasks. Therefore, to improve the power generated from the PV array, this paper suggests a new reconfiguration technique for PV panels using Genetic algorithm (GA) and two main reconfigurable steps based on a switching matrix. In this technique, only the electrical connections of the PV panels are changed while its physical location remains unchanged. To verify the effectiveness of the proposed reconfiguration technique, the system was simulated and tested using MATLAB/SIMULINK software, with four shading patterns. The results were compared with other reconfiguration techniques, namely TCT configuration, competence square (CS), SuDoKu, two-phase array reconfiguration, Genetic algorithm (GA), Particle Swarm Optimization (PSO), and Modified Harris Hawks Optimization (MHHO). The performance of each shading case was also analyzed. Also, a comparative study on performance analysis in real-time application was carried out for each shading pattern. The results prove the superiority of the proposed technique over other techniques for overcoming partial shading. � 2020 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo113806
dc.identifier.doi10.1016/j.enconman.2020.113806
dc.identifier.scopus2-s2.0-85098967039
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85098967039&doi=10.1016%2fj.enconman.2020.113806&partnerID=40&md5=d07e860c6b302bcb3b064924b0fd84bc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26317
dc.identifier.volume230
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleEnergy Conversion and Management
dc.titleComparative analysis of two-step GA-based PV array reconfiguration technique and other reconfiguration techniquesen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS