Multi-objective adaptive clonal selection algorithm for solving optimal power flow problem with load uncertainty
by B. Srinivasa Rao; K. Vaisakh
International Journal of Bio-Inspired Computation (IJBIC), Vol. 8, No. 2, 2016

Abstract: This paper presents a multi-objective adaptive clonal selection algorithm (MOACSA) for solving optimal power flow (OPF) problem with load uncertainty. The multi-objective OPF (MOOPF) problem is generally formulated with minimisation of several objectives by satisfying various constraints. A fast elitist non-dominated sorting and the crowded distance concept have been used to find and manage the Pareto optimal front. Finally, a fuzzy-based mechanism is used for selecting the best compromise solution. The proposed MOACSA method is tested on IEEE 30-bus test system with minimisation of fuel cost, loss and L-index as objectives. Simulation studies are carried out with normal load operation and load uncertainty conditions for MOOPF. The MOACSA method results are compared with non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimisation (MOPSO) and multi-objective differential evolution (MODE) methods without load uncertainty.

Online publication date: Wed, 04-May-2016

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