Multiobjective Optimization Based Framework for Early Stage Design of Modular Multilevel Converter for All-Electric Ship Application

Abstract

The Medium Voltage DC (MVDC) architecture for All Electric Ships (AES) has the potential to provide superior features compared to traditional 60-HZ AC distribution systems in terms of power density, power quality, and system stability. The MVDC system introduces extensive use of power electronics equipment into the shipboard power system (SPS) design that brings complexity to the system design. These power electronics equipment connect the power sources and load centers to the MVDC bus and play a major role in handling system faults. This paper focuses on developing a framework to determine the volume and failure rate of a Modular Multilevel Converter (MMC) for early stage ship design. Two different methodologies (Taguchi method and a genetic algorithm) were used to determine the best design from a robust set of design options. Once the design parameters have been identified, the Taguchi method forms orthogonal array to explore and evaluate designs. At the end of the design cycle, it identifies the best parameters from a large set of design parameters to achieve lower volume and failure rate. These parameters are used as input to the optimization process. This helps to narrow out the number of inputs for optimization algorithm. The Nondominated Sorting Genetic Algorithm II (NSGA-II) has been integrated with converter design tool to minimize the volume and failure rate of MMC. The results show that the optimization algorithm coupled with Taguchi Method provides the lowest volume and failure rate for MMC. One of the goals of early-stage ship design is to develop preliminary design and evaluation of trade space to narrow it down. This paper is expected to aid early-stage ship design of power electronics converter design for MVDC systems in SPS.

Keywords

• AES
• MMC
• MVDC
• NSGA-II
• Taguchi design of experiments
• SPS