TY  - JOUR
ID  - scholars2677
N2  - This paper presents three intelligent evolutionary optimization techniques to investigate the optimal design parameters of a 3-phase furnace transformer. The transformer rating is derived from the operating conditions of a medium size direct arc furnace. Scatter Search (SS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) techniques are employed on the developed nonlinear mathematical model of the transformer for constrained optimization minimizing the cost. The design and analysis programs of the furnace transformer are developed using codes written in C++/C language. The optimal design data results validated by an example show the efficacy of the three intelligent techniques. Among the three methods, the optimal results obtained by GA and PSO techniques show the potential for implementing as efficient search techniques for design optimization of furnace transformers. © 2012 Elsevier Ltd. All rights reserved.
EP  - 1062
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863753760&doi=10.1016%2fj.ijepes.2012.06.019&partnerID=40&md5=3b305d9533def6e2de34559a1252aaff
SN  - 01420615
IS  - 1
JF  - International Journal of Electrical Power and Energy Systems
TI  - Optimal design of a furnace transformer by intelligent evolutionary methods
N1  - cited By 3
VL  - 43
SP  - 1056
A1  - Rama Rao, K.S.
A1  - Karsiti, M.N.
KW  - Arc furnaces; Design and analysis; Design optimization; Evolutionary method; Evolutionary optimizations; Furnace transformers; Intelligent techniques; Medium size; Nonlinear mathematical model; Operating condition; Optimal design; Optimal design parameters; Optimal results; Scatter search; Search technique; Transformer ratings
KW  -  Computer software; Constrained optimization; Genetic algorithms; Mathematical models; Nonlinear programming; Optimal systems; Particle swarm optimization (PSO); Power transformers
KW  -  Furnaces
Y1  - 2012///
AV  - none
ER  -