Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2478
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dc.contributor.authorShekarappa G, Swetha-
dc.contributor.authorMahapatra, Sheila-
dc.contributor.authorRaj, Saurav-
dc.date.accessioned2023-12-18T03:58:52Z-
dc.date.available2023-12-18T03:58:52Z-
dc.date.issued2023-05-
dc.identifier.issn2667-3053-
dc.identifier.urihttps://doi.org/10.1016/j.iswa.2023.200220-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2478-
dc.description.abstractOne of the most important aspects of power system planning is reactive power (VAR- Volt Ampere- Reactive) optimization and voltage control, which has an influence on both economics and stability of the transmission systems. It is perhaps one of the most difficult optimization problem to solve since it is nonlinear and includes both consistent and sporadic choice parameters. The goal is to primarily calculate the cost components, such as operating cost due to energy loss, series compensation (Thyristor-Controlled series Capacitor-TCSC) device operating costs, and actual power loss cost. In the proposed work, to enhance system voltage profile, TCSC is deployed at weak points across several echelon after computing mathematical models for standard benchmark functions. The paper goes into great depth with application of Oppositional based learning on Harris Hawks Optimizer (OHHO). This meta heuristic optimization approach, has been used to solve the VAR optimization issue. Finally, the benchmark functions outputs are thoroughly examined for two test systems like Ward Hale 6 bus system and modified IEEE- 30 bus test system to demonstrate the validity of the proposed hybrid intelligent approach for series compensated FACTS controller.en_US
dc.language.isoenen_US
dc.publisherIntelligent Systems with Applicationsen_US
dc.subjectHarris Hawks optimization (HHO)en_US
dc.subjectOppositional based Harris hawks optimization (OHHO)en_US
dc.subjectReactive power planningen_US
dc.subjectFACTS deviceen_US
dc.titleA Novel Meta-Heuristic Approach for Optimal RPP Using Series Compensated FACTS Controlleren_US
dc.typeArticleen_US
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