Application of dynamic neighborhood small population particle swarm optimization for reconfiguration of shipboard power system

In all-electric navy ships, severe damage or faults may occur during different conditions. As a result, critical loads may suffer from power deficiencies, ultimately leading to a complete system collapse. Therefore, a fast reconfiguration of shipboard power system (SPS) is necessary to serve the critical loads. This work proposes a novel swarm intelligent algorithm based on dynamic neighborhood small population particle swarm optimization (PSO) (DNSPPSO). DNSPPSO is a variant of PSO having fewer numbers of particles and regenerating new solutions within the search space every few iterations. This concept of regeneration in DNSPPSO makes the algorithm fast and greatly enhances its capability. Meanwhile, this algorithm can handle multi-objective problem effectively by using dynamic neighborhood strategy. This technique sorts the objectives and evaluates objectives one by one but retaining the global best solution and fitness so far. Therefore, the strategy converts the multi-objective problem into a single objective optimization problem. The strength of the proposed reconfiguration strategy is demonstrated by an 8-bus test example in Matlab environment comparing with discrete PSO (DPSO), small population PSO (SPPSO) and NSGA-II.