Topology Optimization of Large-Scale Offshore Wind Power Collection System Based on Graph Genetic DMST

Abstract

This paper proposes a graph genetic dynamic minimum spanning tree (DMST) optimization method for the layout designing of large-scale offshore wind farm collector system to minimize total lifecycle cost, considering both current carrying capacity and cable crossing avoiding (CCA) constraints. In this method, DMST is used to generate an initial feasible solution, ensuring a high-quality and reasonable solution at the first. Then Graph Genetic Algorithm (GGA) is applied to optimize these feasible solutions. A partitioning approach is employed to effectively reduce the complexity of the problem. This method can quickly find solutions with low total lifecycle costs while ensuring the feasibility and quality of the solution. Meanwhile, the proposed method is compared with DMST and particle swarm optimization (PSO) based algorithm, and its effectiveness and efficiency are validated through case studies of offshore wind farm projects, especially for large-scale wind farms.

Keywords

• Offshore wind power collection
• cable crossing avoiding
• topology optimization
• dynamic minimum spanning tree
• graph genetic algorithm