With the increasing penetration of the photovoltaic (PV) in the distributed grid network, the dynamic response analysis of the system becomes more and more complex and costs lots of computational time in the simulation. To cut down the computational resources while guaranteeing the accuracy, this paper proposes a data-driven hybrid equivalent model for the dynamic response process of the multiple PV power stations. The data-driven hybrid equivalent model contains the simple equivalent model and data-driven error correction model. In the equivalent model, the distributed PV power stations in the same branch are equivalent to one power station model based on the parameter equivalence and feeder equivalence. The data-driven error correction model tracks and corrects the difference of dynamic response between the equivalent model and precise model. The ensemble Gated Recurrent Unit (GRU) model based on the bagging ensemble structure utilizes the simple equivalent dynamic response as input to learn the dynamic response errors. The simulation results validate the super-performance of the proposed model both in the response speed and accuracy.

随着光伏（PV）在分布式网格网络中的渗透率的提高，系统的动态响应分析变得越来越复杂，并且在仿真中花费了大量的计算时间。为了在保证计算精度的同时减少计算资源，提出了一种数据驱动的混合等效模型，用于多个光伏电站的动态响应过程。数据驱动的混合等效模型包含简单等效模型和数据驱动的纠错模型。在等效模型中，基于参数等价和馈线等价，同一分支中的分布式光伏电站等效于一个电站模型。数据驱动的纠错模型跟踪并纠正等效模型和精确模型之间的动态响应差异。基于装袋集成结构的集成门控循环单元（GRU）模型利用简单的等效动态响应作为输入来学习动态响应误差。仿真结果验证了所提模型在响应速度和准确性上的超强性能。