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.
International Journal of Bio-Inspired Computation, 2016 Vol.8 No.2, pp.67 - 83
Available online: 04 May 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article