【摘要】 为了提升风电并网系统的暂态仿真分析效率，需要建立风电场等值模型。现有直驱风电场等值模型存在难以适用于预想故障分析和计算效率低问题。针对该问题，提出一种机理-数据混合驱动的直驱风电场分群等值方法。首先，分析了直驱风机的故障后暂态响应特性，并推导了基于风机初始风速及机端故障稳态电压的风电机组分群方法。其次，提出了一种基于多层图卷积神经网络的机端故障稳态电压高效预测方法，该预测模型能够适应不同的风电场和电网拓扑。进一步，构建了风电场的三机等值模型。最后，仿真验证了所提出的分群等值方法的正确性。更多还原

【Abstract】 To improve the efficiency of transient simulation and analysis of wind power grid-connected systems, a wind farm equivalence model is needed. The existing direct-driven wind farm equivalence model has problems such as it is difficult to be applied to post-fault analysis and low computational efficiency. To address this problem, this paper proposes a novel direct-driven wind farm clustering and equivalenting method driven by a mechanism-data hybrid approach. Firstly, the post-fault transient response characteristics of direct-driven permanent magnet synchronous generators(PMSG) were analyzed, and a method of wind turbine group clustering based on the initial wind speed and the post-fault steady-state voltage at the terminal of each PMSG was derived. Secondly, an efficient prediction method of the post-fault steady-state voltage at the terminal of each PMSG based on multi-layer graph convolutional networks was proposed, and the prediction model can be adapted to different wind farms and power grid topologies. Further, a multi-machine equivalent model was constructed for direct-driven wind farms. Finally, the correctness of the proposed subgroup clustering and equivalenting method were verified by simulation.更多还原