Rat Swarm Versus Particle Swarm Intelligent Optimization Algorithms for Maximum Power Point Tracking in Designing Energy-Efficient Solar Systems

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dc.IAW.KSANA
dc.IR.KSAENERGY
dc.SDGs.KSASDG 9
dc.conference.date2025-03-07
dc.conference.locationSingapore
dc.conference.name9th International Conference on Green Energy and Applications (ICGEA)
dc.contributor.alumnae0
dc.contributor.authorHussein, Aziza
dc.contributor.departmentElectrical and Computer Engineering
dc.contributor.firstauthorAnwar, Naveed
dc.contributor.labElectronics Lab
dc.contributor.pgstudent0
dc.contributor.researcherDepartment Collaboration
dc.contributor.ugstudent0
dc.date.accessioned2025-06-18T09:27:56Z
dc.date.available2025-06-18T09:27:56Z
dc.date.issued2025-05-28
dc.description.abstractRecently, Photovoltaic (PV) devices generate electricity directly from sunlight, so their integration into urban infrastructure will not only generate energy but also reduce carbon emissions. Due to those advantages, solar energy plays a crucial role in developing smart cities. However, the efficiency of PV systems is heavily dependent on their ability to operate at the Maximum Power Point (MPP), which represents the bias potential at which the solar cell outputs the maximum net power. This paper employs an intelligent optimization algorithm, RAT Swarm, to find the optimal voltage the PV system can operate to produce maximum power. MATLAB software is used to model and simulate the system using the Rate Swarm algorithm. The simulation results show that the RAT based MPPT has been capable of achieving the capability to precisely track the maximum power point and to maximize the power output of the PV system. Furthermore, the same software is employed to implement and simulate the well-known particle swarm optimization algorithm for comparison. Convergence speed, trading accuracy as well as the stability of power, voltage and current under both uniform and time varying irradiance conditions are also considered in the comparison. Comparative evaluation indicates that RSO is a viable option for PSO owing to faster convergence. Nevertheless, the stability of RSO and the absence of an oscillation in the power output have to be investigated. These improvements would enable RSO to become optimally effective, achieving fast convergence and stable output power for energy capture from PV systems.
dc.identifier.citationN. Anwar and A. I. Hussein, "Rat Swarm Versus Particle Swarm Intelligent Optimization Algorithms for Maximum Power Point Tracking in Designing Energy-Efficient Solar Systems," 2025 9th International Conference on Green Energy and Applications (ICGEA), Singapore, Singapore, 2025, pp. 1-6,
dc.identifier.doihttps://ieeexplore.ieee.org/document/11009856
dc.identifier.urihttps://repository.effatuniversity.edu.sa/handle/20.500.14131/2126
dc.publisherIEEE
dc.research.classifApplied
dc.source.indexScopus
dc.subjectTECHNOLOGY::Electrical engineering, electronics and photonics
dc.subject.KSAEconomies of the Future
dc.titleRat Swarm Versus Particle Swarm Intelligent Optimization Algorithms for Maximum Power Point Tracking in Designing Energy-Efficient Solar Systems
dc.title.projectRat Swarm Versus Particle Swarm Intelligent Optimization Algorithms for Maximum Power Point Tracking in Designing Energy-Efficient Solar Systems
dspace.entity.typePublication
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