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Application of Coulomb’s and Franklin’s laws algorithm to solve large-scale optimal reactive power dispatch problems

dc.contributor.authorGhasemi M
dc.contributor.authorAkbari E
dc.contributor.authorFaraji Davoudkhani I
dc.contributor.authorRahimnejad A
dc.contributor.authorAsadpoor MB
dc.contributor.authorGadsden SA
dc.contributor.departmentMechanical Engineering
dc.date.accessioned2025-02-27T14:48:31Z
dc.date.available2025-02-27T14:48:31Z
dc.date.issued2022-12
dc.date.updated2025-02-27T14:48:31Z
dc.description.abstractThis study focuses on the application of Coulomb’s and Franklin’s laws algorithm (CFA) to solving large-scale optimal reactive power dispatch (LS-ORPD) problems. The CFA optimizer acts on the basis of the charged particles interactions. The ever-increasing effects of ORPD problems for safe and reliable operation of electrical power grids is an important area of study. Such problems are classified as nonlinear optimization problems; the aim of which is to minimize the active power loss through tuning of several control variables. Firstly, the performance of CFA optimizer in solving high-dimensional problems is investigated using standard benchmark problems. Moreover, we apply the CFA optimizer for solving large-scale ORPD problems based on different constraints in three IEEE standard power systems. According to the results, the proposed optimizer offers a more accurate solution when compared with other methods found in the literature. Finally, an early attempt is carried out for improving CFA optimizer, which is tested on benchmark and ORPD problems and yields promising outcome in reaching a more powerful variant of CFA.
dc.identifier.doihttps://doi.org/10.1007/s00500-022-07417-w
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.urihttp://hdl.handle.net/11375/31140
dc.publisherSpringer Nature
dc.subject46 Information and Computing Sciences
dc.titleApplication of Coulomb’s and Franklin’s laws algorithm to solve large-scale optimal reactive power dispatch problems
dc.typeArticle

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