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Genetic Algorithm based Fuzzy Logic Controller for Optimal Charging-Discharging of Energy Storage in Microgrid applications

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dc.contributor.authorFaisal M.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorMuttaqi K.M.en_US
dc.contributor.authorid57215018777en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid55582332500en_US
dc.date.accessioned2023-05-29T09:39:07Z
dc.date.available2023-05-29T09:39:07Z
dc.date.issued2022
dc.descriptionBattery management systems; Charging (batteries); Computer circuits; Energy storage; Fuzzy logic; Genetic algorithms; Membership functions; Microgrids; Particle swarm optimization (PSO); Secondary batteries; Water treatment; Battery state of charge; Charging/discharging; Fuzzy logic controllers; Microgrid; Optimal charging; Power; Renewable technology; States of charges; Storage systems; Swarm optimization; Controllersen_US
dc.description.abstractMicrogrid (MG) concept with renewable technologies have the challenges of supplying reliable power considering the intermittent nature of the sources. Energy storage system (ESS) has become a viable solution to control the power fluctuation and thus providing the reliable power to the consumer. However, commonly used charging-discharging control techniques have the limitations of solving overcharging or over-discharging problem, fast charging capability, and rapid response time. To overcome these problems, fuzzy logic controller (FLC) has been proposed to control the charging-discharging due to its easy implementation, no mathematical calculation, and simplicity. However, existing FLC technologies have the limitations in considering the battery control parameters, and selecting the safe operating region (20% to 80%) of the battery state of charge (SOC). Therefore, this research proposes an improved FLC considering the available power from grid and distributed sources, load demand, battery SOC and temperature. To improve the performance of the controller, membership functions (MFs) of the FLC have been optimized by using genetic algorithm (GA). To prove the superiority of GA, another widely used optimization algorithm, particle swarm optimization (PSO) is applied with the same load variation. Obtained results show that, the minimum and maximum SOC level for fuzzy-GA only system has been improved compared to fuzzy only and fuzzy-PSO system. Therefore, it can be concluded that, the developed model works efficiently in controlling the charging and discharging of the battery. The authors are in progress to apply the controller system for MG connected waste water treatment plant. � 2022 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/IAS54023.2022.9939768
dc.identifier.scopus2-s2.0-85142794915
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85142794915&doi=10.1109%2fIAS54023.2022.9939768&partnerID=40&md5=2b0f644f2e8c0292c897e23048ea1dcc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27057
dc.identifier.volume2022-October
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleConference Record - IAS Annual Meeting (IEEE Industry Applications Society)
dc.titleGenetic Algorithm based Fuzzy Logic Controller for Optimal Charging-Discharging of Energy Storage in Microgrid applicationsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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