Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/15053
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dc.contributor.authorRaj, Saurav-
dc.contributor.authorMahapatra, Sheila-
dc.contributor.authorShiva, Chandan Kumar-
dc.contributor.authorBhattacharyya, Biplab-
dc.date.accessioned2024-04-08T04:11:04Z-
dc.date.available2024-04-08T04:11:04Z-
dc.date.issued2021-
dc.identifier.citationVol. 10, No. 2; pp. 74-103en_US
dc.identifier.issn2160-9500-
dc.identifier.issn2160-9543-
dc.identifier.urihttp://dx.doi.org/10.4018/IJEOE.2021040104-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/15053-
dc.description.abstractIn this article, innovative algorithms named as salp swarm algorithm (SSA) and hybrid quasi-oppositional SSA (QOSSA) techniques have been proposed for finding the optimal coordination for the solution of reactive power planning (RPP). Quasi-oppositional based learning is a promising technique for improving convergence and is implemented with SSA as a new hybrid method for RPP. The proposed techniques are successfully implemented on standard test systems for deprecation of real power losses and overall cost of operation along with retention of bus voltages under acceptable limits. Optimal planning has been achieved by minimizing reactive power generation and transformer tap settings with optimal placement and sizing of TCSC. Identification of weakest branch in the power network is done for optimal TCSC placement and is tendered through line stability index method. Optimal TCSC placement renders a reduction in transmission loss by 8.56% using SSA and 8.82% by QOSSA in IEEE 14 bus system and 7.57% using SSA and 9.64% by QOSSA in IEEE 57 bus system with respect to base condition.en_US
dc.language.isoenen_US
dc.publisherInternational Journal of Energy Optimization and Engineeringen_US
dc.publisherIgi Globalen_US
dc.subjectActive Power Lossen_US
dc.subjectLine Stability Index (Lsi)en_US
dc.subjectQuasi-Oppositional Concepten_US
dc.subjectReactive Power Planning (Rpp)en_US
dc.subjectSalp Swarm Algorithm (Ssa)en_US
dc.subjectThyristor Controlled Series Compensator (Tcsc)en_US
dc.titleImplementation and Optimal Sizing of Tcsc for the Solution of Reactive Power Planning Problem Using Quasi-Oppositional Salp Swarm Algorithmen_US
dc.typeArticleen_US
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