Wind farms (WFs) are important components of smart grid. The modeling and optimal planning of the WF is preliminary before its construction. In this article, a bi-level multi-objective optimization framework is presented, with the aim of simultaneously designing the configuration of wind turbines (WTs) as well as the topology of electrical collector system in an offshore WF. The installation capacity of the WF, the positioning of the WTs and the planning scheme of the electrical system are balanced to achieve a better performance of the WF. In this proposal, there is an outer layer along with two inner layers. The objectives of the outer-layer model are the maximization of the WF’s daily profit rate, the daily average capacity factor, and power quality. It is tackled by the Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The objectives of the two inner layer models are to determine the topology of the electrical system and the generation schedule of other generators, and are solved by means of the Binary Particle Swarm Optimization (BPSO) algorithm and the quadratic programming (QP) method respectively. The WF is assumed to be connected to the IEEE-24 bus test system. The simulation results validate the adaptability and effectiveness of the proposed approach with the main factors that affect the WF layout being analyzed.

中文翻译：

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海上风电场风电机组微布线的综合优化

风电场（WFs）是智能电网的重要组成部分。WF的建模和优化计划在其构建之前是初步的。本文提出了一种双层多目标优化框架，旨在同时设计海上WF中的风力涡轮机（WT）的配置以及集电器系统的拓扑。WF的安装容量，WT的位置和电气系统的规划方案是平衡的，以实现WF更好的性能。在此建议中，有一个外层以及两个内层。外层模型的目标是最大程度地提高WF的每日利润率，每日平均容量因子和电能质量。它由非支配排序遗传算法III（NSGA-III）解决。两个内层模型的目的是确定电气系统的拓扑结构和其他发电机的发电计划，并分别通过二进制粒子群优化算法（BPSO）和二次规划（QP）方法进行求解。假定WF已连接到IEEE-24总线测试系统。仿真结果验证了所提出方法的适应性和有效性，并分析了影响WF布局的主要因素。