Title: Application of swarm intelligent techniques with mixed variables to solve optimal power flow problems
Authors: S. Surender Reddy; B.K. Panigrahi
Department of Railroad and Electrical Engineering, Woosong University, Daejeon - 300718, South Korea
Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
Abstract: This paper proposes a new swarm based evolutionary algorithm called LBEST PSO with dynamically varying sub-swarms (LPSO DVS). Swarm based algorithms are meta-heuristic search methods whose mechanics are inspired by the collaborative behaviour of biological populations. The performance of four swarm based algorithms, i.e., particle swarm optimisation (PSO), fuzzy adaptive particle swarm optimisation (FAPSO), fitness distance ratio particle swarm optimisation (FDRPSO) and LPSO DVS are also compared with genetic algorithm (GA) and improved GA when applied to the power system optimal power flow (OPF) problem. OPF optimises the power system operating objective function, while satisfying the set of system operating constraints. The objective functions considered in this OPF problem are fuel cost (FC) minimisation, voltage stability enhancement index (VSEI) minimisation, transmission loss minimisation (LM) and voltage deviation (VD) minimisation. Simulation results for the IEEE 30 bus system are presented and the comparison is made among the numerical results obtained using the different evolutionary algorithms.
Keywords: evolutionary algorithms; fuzzy sets; genetic algorithm; optimal power flow; OPF; particle swarm optimisation; PSO.
Int. J. of Bio-Inspired Computation, 2017 Vol.10, No.4, pp.283 - 292
Available online: 03 Nov 2017