2021
DOI: 10.1002/2050-7038.12756
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New approaches for regulation of solid oxide fuel cell using dynamic condition approximation and STATCOM

Abstract: Summary A new dynamic condition approximation method, which provides the current feedback, in collaboration with STATCOM regulation approach is presented to diminish the variation in voltage of solid oxide fuel cell (SOFC) electrical energy generators in connection to the compound power network in the time of faults. The suggested regulation approach is contrasted with the other present regulation techniques and the results show its advantage over the others in mitigating voltage fluctuation, inaccuracy and gu… Show more

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Cited by 17 publications
(4 citation statements)
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“…3. As can be observed in this research study, the suggested improved golden jackal optimization algorithm provides the highest efficiency while consuming the least amount of power for the system [51]. The graphical concept of the diagram, as well as the outcomes of optimization for the improved golden jackal optimization algorithm, is illustrated in Fig.…”
Section: The Results Of the Simulationmentioning
confidence: 81%
“…3. As can be observed in this research study, the suggested improved golden jackal optimization algorithm provides the highest efficiency while consuming the least amount of power for the system [51]. The graphical concept of the diagram, as well as the outcomes of optimization for the improved golden jackal optimization algorithm, is illustrated in Fig.…”
Section: The Results Of the Simulationmentioning
confidence: 81%
“…[ 5 ], an improved marine predators metaheuristic algorithm was used for the sizing of a combined heat/power with PV/Wind/Storage system. A metaheuristic method was also considered to handle the problem of voltage fluctuation in PV system [ 27 ]. A hybrid technique of world cup optimization combined with fluid search optimization was proposed for the identification of the parameter of proton exchange membrane fuel cell [ 28 ].…”
Section: Methodsmentioning
confidence: 99%
“…This has been trained by a small representative sample achieved by any sampling technique [ 24 ]. Nevertheless, it is not possible to define the correct training sample size previously due to that it depends on the problem [ 25 ]. Indeed, until now, trial and error is used to define this size for a particular case study building.…”
Section: Introductionmentioning
confidence: 99%