Title: Multi-objective adaptive clonal selection algorithm for solving optimal power flow problem with load uncertainty

Authors: B. Srinivasa Rao; K. Vaisakh

Addresses: Department of Electrical and Electronics Engineering, V.R. Siddhartha Engineering College, Vijayawada-520007, AP, India ' Department of Electrical Engineering, AU College of Engineering, Visakhapatnam-530003, AP, India

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.

Keywords: artificial immune system; AIS; adaptive clonal selection algorithm; ACSA; optimal power flow; OPF; bio-inspired computation; load uncertainty; multi-objective optimisation; fuel cost minimisation; power loss minimisation; voltage stability enhancement; genetic algorithms; NSGA-II; particle swarm optimisation; PSO; differential evolution; simulation.

DOI: 10.1504/IJBIC.2016.076325

International Journal of Bio-Inspired Computation, 2016 Vol.8 No.2, pp.67 - 83

Received: 05 Mar 2013
Accepted: 14 Mar 2014
Published online: 04 May 2016 *

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