Implementation of an Intelligent Reconfiguration Algorithm for an Electric Ship Power System

TL;DR: A fast, intelligent reconfiguration algorithm, where Pareto optimal solutions are obtained by Small Population based Particle Swarm Optimization (SPPSO) from two conflicting objective functions.

Abstract: In all-electric navy ships, severe damages or faults may occur during battle conditions. This might even affect the generators and as a result, critical loads might suffer from power deficiency for a long time and ultimately lead to a complete system collapse. A fast reconfiguration of the power path is therefore necessary in order to serve the critical loads and to maintain a proper power balance in ship power system. This paper proposes a fast, intelligent reconfiguration algorithm, where Pareto optimal solutions are obtained by Small Population based Particle Swarm Optimization (SPPSO) from two conflicting objective functions. From the Pareto set, the final solution is chosen depending on users' preference. SPPSO is a variant of PSO which works with very few numbers of particles with a regeneration of new solutions within the search space after few iterations. This concept of regeneration in SPPSO make the algorithm really fast and enhances its capability to a large extent. The strength of the proposed reconfiguration strategy is tested in Real-Time Digital Simulator (RTDS) environment.