Intelligent Nonlinear PID-Controller Combined With Optimization Algorithm for Effective Global Maximum Power Point Tracking of PV Systems

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dc.authoridAboRas, Kareem/0000-0003-0485-468X
dc.authoridDAGAL, IDRISS/0000-0002-2073-8956
dc.authoridAlwesabi, Ibrahim/0000-0003-1848-427X
dc.contributor.authorIbrahim, Al-Wesabi
dc.contributor.authorFang, Zhijian
dc.contributor.authorLi, Rui
dc.contributor.authorZhang, Weilong
dc.contributor.authorXu, Jiazhu
dc.contributor.authorZahir, Vseem
dc.contributor.authorDagal, Idriss
dc.date.accessioned2025-03-09T10:48:50Z
dc.date.available2025-03-09T10:48:50Z
dc.date.issued2024
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractMany advanced techniques efficiently harvest the global maximum power (GMP) of the photovoltaic (PV) system, including machine learning techniques and metaheuristic algorithms based maximum power point tracking (MPPT). Nevertheless, they have shortcomings such as sluggish convergence and local maxima power (LMP) trapping. Combining techniques improves productivity. This work proposes an intelligent nonlinear proportional-integral-derivative (NPID) controller coupled with hybrid salp particle swarm optimization algorithm (SPSOA) to successfully harvest the GMP of PV system. The SPSOA-NPID controller's performance is measured in regard to settle time, rising time, overshooting, peak time, undershoot, rotor rotation speed, and GMP under varied realistic irradiation and temperature profiles. In this work, the optimum parameter settings for the proposed NPID and the basic PID controllers were obtained utilizing the hybrid genetic algorithm and PSO (GA-PSO) techniques. Simulation findings proved the success and robustness of the SPSOA-NPID-based MPPT controller followed by GA-PSO-PID, GA-PID, GA-NPID, PSO-PID, PSO-NPID, P&O and INC respectively. The proposed SPSOA-NPID method obtained an average efficiency of (0.9946), the lowest average ripples (8.214 W), and the fastest average tracking time (0.052 s). Lastly, a hardware-in-loop (HIL) experimental carried out to confirm that the proposed SPSOA-NPID control can be practically implemented. Consequently, it is determined that the proposed SPSOA-NPID based GA-PSO control system is a potential MPPT approach based on the thorough research that have been given.
dc.description.sponsorshipNational Natural Science Foundation of China [52077069]
dc.description.sponsorshipThis work was supported by the National Natural Science Foundation of China under Grant 52077069.
dc.identifier.doi10.1109/ACCESS.2024.3513355
dc.identifier.endpage185290
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85212233317
dc.identifier.scopusqualityQ1
dc.identifier.startpage185265
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3513355
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4672
dc.identifier.volume12
dc.identifier.wosWOS:001377296900013
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250310
dc.subjectConvergence
dc.subjectOscillators
dc.subjectMaximum power point trackers
dc.subjectMetaheuristics
dc.subjectFluctuations
dc.subjectTuning
dc.subjectPD control
dc.subjectGenetic algorithms
dc.subjectElectrical engineering
dc.subjectCosts
dc.subjectSPSOA based MPPT
dc.subjectnonlinear PID
dc.subjecthybrid GA-PSO
dc.subjectDC motor and PV system
dc.titleIntelligent Nonlinear PID-Controller Combined With Optimization Algorithm for Effective Global Maximum Power Point Tracking of PV Systems
dc.typeArticle

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