基于数据驱动的海上风电集电网无功功率分配优化策略研究

Optimization Strategy of Reactive Power Distribution in Offshore Wind Power Collector Network Based on Data-driven

海上风电大规模集群化发展造成风电场内电量损失增加。针对海上风电集电网损耗大、无功功率调度耗时高的问题,提出一种基于数据驱动的海上风电集电网无功功率分配优化策略。首先,考虑双馈风电机组(DFIG)无功功率调节能力,构建以降损率和电压偏差综合最小化为目标的无功功率分配优化模型,设计风电机组的最优无功功率分配策略;其次,基于集电网的电气拓扑和风电机组的最优无功功率分配策略,采用图卷积网络离线训练无功功率分配关系网络;最后,采用基于先验知识的粒子群优化算法对图卷积网络的输出进行监督,以增强模型的实用性。仿真分析表明,所提方法能有效降低集电网损耗和计算时间,兼顾海上风电无功功率实时优化降损与电压控制需求。

The large-scale cluster development of offshore wind power has led to an increase in power loss in wind farms.In order to solve the problems of large loss and high time-consuming reactive power dispatching in offshore wind farms,a data-driven strategy of optimization reactive power distribution in offshore wind power collector network is proposed.Firstly,considering the reactive power ability of doubly-fed wind turbines,an optimization model of reactive power allocation with the goal of comprehensive minimization of loss reduction rate and voltage deviation is constructed,and the optimal reactive power distribution strategy of DFIG is designed.Secondly,based on the electrical topology and optimal allocation strategy of offshore wind power collector network,the graph convolutional network is used to train the reactive power distribution relationship network offline.Finally,the particle swarm optimization algorithm based on prior knowledge is applied to refine the model’s output for practical use.Simulations demonstrate the effectiveness of the proposed method in reducing losses and computation time while meeting reactive power and voltage control needs for offshore wind energy.