Exploring Methods for Studying Recoverability of Shipboard Electrical Power Distribution Systems

Abstract

Advancement of the technology included on ships, is changing the nature of loads installed on ships. Not only are there more loads than installed power generation, but advanced electrically powered weapons and sensors are changing the profile of the installed loads. These are challenges for future shipboard power systems design. Challenges which must be solved with careful distribution system planning, energy storage integration, and selection of individual power electronic converters within the ship. Different topologies and distribution systems have differing fault isolation and recovery (FIR) capability sets, which affects ship survivability. Furthermore, different FIR capability sets, operate at different speeds. The speed of a system's survivability functionality dictates the amount of energy storage needed. A goal of this work is to enable such topological comparisons.A challenge of making such topological comparisons is simulating shipboard power distribution systems. For the purposes of FIR the simulation must have accurate grounding and realistic cabling to enable study of line to line and line to ground faults in a floating system. This means the simulation must not only have differential mode behavior but accurate common mode behavior. As well as the challenge presented by the length of time required to simulate large systems by conventional offline simulation methods. By finding means to reduce simulation run times, via the use of real time simulations methods. While keeping in mind that each module has an effect on FIR efforts, which implies that the controls of each sub-system must be capable of enabling FIR topological comparisons. In this work a set of simulations of a realistic ship auxiliary power system with full switch models, including medium voltage and low voltage distribution, were developed and tested. This lays the ground work for future topological comparisons enabled by the simulation methodology and state machine based controls.