Publication:
Photovoltaic Array Reconfiguration System for Maximizing the Harvested Power Using Population-Based Algorithms

Simple item page
dc.citedby55
dc.contributor.authorBabu T.S.en_US
dc.contributor.authorYousri D.en_US
dc.contributor.authorBalasubramanian K.en_US
dc.contributor.authorid56267551500en_US
dc.contributor.authorid56688582500en_US
dc.contributor.authorid56654475000en_US
dc.date.accessioned2023-05-29T08:13:02Z
dc.date.available2023-05-29T08:13:02Z
dc.date.issued2020
dc.descriptionDispersions; Genetic algorithms; Real time control; Renewable energy resources; Non-renewable energy resources; Optimization algorithms; Photovoltaic arrays; Photovoltaic systems; Population-based algorithm; Population-based optimization; Real-time implementations; Reconfiguration system; Photovoltaic cellsen_US
dc.description.abstractMassive infiltration of photovoltaic (PV) systems into electric supply networks creates numerous challenges in the present era, as the PV systems become an alternative to non-renewable energy resources. Partial shading, nevertheless, is an essential problem which affects the productivity and life of PV plants. PV reconfiguration is known as a powerful technique to resolve this effect. It is achieved by rearranging the PV modules according to their temperature and levels of shade. Therefore, in this paper, we have utilized three simple population-based optimization algorithms that are known as the flow regime algorithm (FRA), the social mimic optimization algorithm (SMO), and the Rao optimization algorithm to dynamically restructure the PV array. The effectiveness of the proposed algorithms is evaluated using several metrics such as fill factor, mismatch losses, percentage of power loss, and percentage of power enhancement. Besides, the results obtained are compared with a regular total-cross-tied (TCT) connection and recently published techniques such as the competence square (CS) and genetic algorithm (GA). Furthermore, to demonstrate the suitability of proposed approaches in real-time implementation, real-time irradiation data of a particular location are considered and fed into the proposed algorithms for effective shade dispersion. After successful shade dispersion, the total energy generated using the three proposed algorithms is calculated and compared with the TCT reconfigured system for one year. The presented energy calculations and revenue generation confirm that the power produced by the proposed FRA technique is 13% higher than that generated by the TCT configuration. Furthermore, the presented PV characteristics show a reduced number of multiple peaks in the system. Thus, the proposed FRA technique can be endorsed as a technique that is superior to other existing methods. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9112214
dc.identifier.doi10.1109/ACCESS.2020.3000988
dc.identifier.epage109624
dc.identifier.scopus2-s2.0-85087383438
dc.identifier.spage109608
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85087383438&doi=10.1109%2fACCESS.2020.3000988&partnerID=40&md5=c771a8b873cee620f6a1912fe7820c33
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25705
dc.identifier.volume8
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titlePhotovoltaic Array Reconfiguration System for Maximizing the Harvested Power Using Population-Based Algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS