Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/675
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dc.contributor.authorShekarappa G, Swetha-
dc.contributor.authorSheila, Mahapatra-
dc.date.accessioned2023-05-22T05:39:42Z-
dc.date.available2023-05-22T05:39:42Z-
dc.date.issued2022-10-07-
dc.identifier.urihttps://doi.org/10.1504/IJBIC.2022.126290-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/675-
dc.description.abstractThis article proposes a metaheuristic nature inspired hybridisation of oppositional-based marine predators' algorithm (OMPA) hybridised with Harris Hawks' optimisation (HHO) which is together implemented as OMPA-HHO on standard IEEE 57 bus system. The algorithms in quest space, which is hybridised, is mutated by incorporating MPA with oppositional-based learning (OBL) technique in addition to obtaining improved evaluation for the dominant solution. The validation of the proposed algorithm has been with compilation on 23 standardised benchmark functions. Reactive power planning is a complex issue for power system researchers and engineers and proposed OMPA-HHO is applied on standard test system to verify its efficacy and simulation results reflect the improved performance. The results validate the scalability, repeatability and sturdiness of the proposed algorithm which can be considered as a superior one in complex optimisation problems.en_US
dc.publisherInderscience Enterprises Ltden_US
dc.subjecttransmission lossen_US
dc.subjectreactive power planningen_US
dc.subjectHarris Hawks' optimisationen_US
dc.subjectRPPen_US
dc.titleVAR strategic planning for reactive power using hybrid soft computing techniquesen_US
Appears in Collections:Journal Articles

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