Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/789
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dc.contributor.authorMahapatra, Sheila-
dc.contributor.authorMalik, Nitin-
dc.contributor.authorRaj, Saurav-
dc.contributor.authorSrinivasan, Mohan Krishna-
dc.date.accessioned2023-05-26T10:51:29Z-
dc.date.available2023-05-26T10:51:29Z-
dc.date.issued2021-09-25-
dc.identifier.urihttps://link.springer.com/article/10.1007/s13198-021-01334-1-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/789-
dc.description.abstractThis paper works on the solution to Voltage Constrained Optimal Power Flow (VCOPF) problem with the requisite allocation of thyristor-controlled series compensator (TCSC) in IEEE 6-bus and 14-bus transmission network to cut down system power losses and to revamp bus voltage profile. The Newton–Raphson algorithm computes the power flow under normal and overloaded operating conditions. The optimal TCSC location is identified by the Cuckoo Search algorithm (CS) and optimal size is determined using an ant-lion optimizer (ALO). The quadratic fuel cost is chosen as the objective and is subjugated to the equality and inequality constraints. The proposed methodology is validated by contrasting the results to the other hybrid methods such as Fuzzy-Gravitational search algorithm (F-GSA), Improved GSA-Firefly algorithm (IGSA-FA) and Radial basis function neural network-GSA (RBFNN-GSA). The statistical analysis is also carried out for validating the efficacy of the algorithm when compared with other reported methods in the literature. The simulation results obtained on standard test systems manifest the improved performance of proposed hybrid cuckoo search and ant lion optimizer (CS-ALO) in comparison with the other optimization techniques that have emerged in the recent state-of-the-art-literature.en_US
dc.language.isoenen_US
dc.publisherIDEASen_US
dc.titleConstrained optimal power flow and optimal TCSC allocation using hybrid cuckoo search and ant lion optimizeren_US
dc.typeArticleen_US
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