Research on reliability-based topology optimization of offshore wind farm collection and delivery

Large-scale expansion is observed in the development of offshore wind turbines, with increasing complexity in offshore convergence. The conventional prim algorithm and kruscal algorithm suffer from issues such as significant disparity in loop load, instability of the algorithm, and limited search efficiency. Consequently, they are not suitable for addressing dynamic programming problems. Nevertheless, a dynamic solution is imperative to effectively address the interdependence between system cost and topological structure in offshore wind power collection. Hence, based on the spatial location distribution characteristics of wind farms and considering the random fluctuation characteristics of wind power, this paper proposes a hybrid greedy genetic algorithm combining local search and hybrid repair optimization operators. Additionally, a topology optimization technique is developed with the objective of maximizing overall economic advantages. After the analysis of the real case in Jiangsu Province, it is evident that the proposed optimization model yields a topological structure cost that is 8.38% lower than both HVDC and FFTS schemes. This outcome holds significant technical and economic value, offering valuable insights for offshore wind farm planning and construction.