Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/836
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dc.contributor.authorShekarappa G, Swetha-
dc.contributor.authorMahapatra, Sheila-
dc.date.accessioned2023-06-07T05:44:38Z-
dc.date.available2023-06-07T05:44:38Z-
dc.date.issued2021-09-11-
dc.identifier.urihttps://doi.org/10.1080/15325008.2021.1970060-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/836-
dc.description.abstractThis study proposes the application of an efficient and hybrid meta-heuristic algorithm of Harris Hawk-Particle Swarm Optimizer (HHOPSO) for solving voltage constrained reactive power planning (VCRPP) problem. The prime objective of present work is diminishing the transmission loss and overall operating cost coupled with retention of voltage consistency and enhanced congestion management in transmission lines. The efficacy of the proposed algorithm is tested on standard IEEE 57 bus test system with optimal transformer tap settings, reactive generation control, generator voltage control and optimal control of Var sources. The best location of Var sources is determined by Voltage Collapse Proximity Indicator (VCPI) method. Simulation results are generated under normal and variable reactive power loading condition which makes the reactive power planning problem more challenging and validates the robustness of the proposed algorithm. The results of proposed algorithm when compared with the other optimization algorithms surfaced in contemporary state of art literature yields superior solution in maintaining diversity and solution optimality.en_US
dc.language.isoenen_US
dc.publisherTaylor and Francisen_US
dc.subjectReactive power planningen_US
dc.subjectHarris Hawks optimizeren_US
dc.subjectParticle swarm optimizeren_US
dc.subjectVoltage collapse proximity indicatoren_US
dc.titleVoltage Constrained Reactive Power Planning Problem for Reactive Loading Variation Using Hybrid Harris Hawk Particle Swarm Optimizeren_US
dc.typeArticleen_US
Appears in Collections:Journal Articles

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