Title: Application of adaptive clonal selection algorithm to solve multi-objective optimal power flow with wind energy conversion systems
Authors: Balusu Srinivasa Rao
Addresses: EEE Department, VR Siddhartha Engineering College, Vijayawada-520007, Andhra Pradesh, India
Abstract: This paper presents the application of adaptive clonal selection algorithm to solve single and multi-objective optimal power flow (OPF) problems with the incorporation of wind energy conversion systems. As the wind power is intermittent in nature it requires an appropriate tool for OPF problem. Minimisation of generation cost of thermal as well as wind units, transmission loss and voltage stability index are considered as three conflicting objectives for optimisation. A fast elitist non-dominated sorting and crowding distance techniques have been used to find and manage the Pareto optimal front. Further, a fuzzy-based mechanism has been applied to select a best compromise solution from the Pareto set. The proposed method has been tested on standard IEEE 30-bus test system having three conventional and three wind power generators. The simulation results are compared with three other standard algorithms such as non-dominated sorting genetic algorithm-II, multi-objective particle swarm optimisation and multi-objective differential evolution.
Keywords: artificial immune system; AIS; clonal selection algorithm; CSA; optimal power flow; multi-objective adaptive CSA; MOACSA; wind energy conversion systems; WECS; voltage stability index; optimal power flow; OPF; wind power; fuzzy logic; simulation; NSGA; genetic algorithms; multi-objective PSO; particle swarm optimisation; multi-objective differential evolution.
International Journal of Power and Energy Conversion, 2017 Vol.8 No.3, pp.322 - 342
Received: 26 Nov 2015
Accepted: 15 Apr 2016
Published online: 09 Jul 2017 *