双馈风电场动态等值建模依赖于特定扰动，难以获取普适性强的等值模型。针对该问题，本文提出了一种基于数据驱动的双馈风电场动态等值建模方法。首先，将双馈风电机组的数学模型简化为一组方程式；其次，选择与该方程式具有相似性的神经网络，并进行合理组合，构建了包含特征记忆层、信息流加速层和数据关系映射层的残差组合神经网络，用于对风电场详细模型进行等值简化；然后，使用遗传算法对该组合网络三个组成部分的主要参数进行优化。以河南某典型风电场为例进行测试分析，结果表明，所提出基于残差组合神经网络的风电场建模方法能够精准刻画风电场输出响应特性，具有更高的建模精度。

Dynamic equivalent modeling of doubly-fed wind farms relies on specific disturbances, making it challenging to obtain universally applicable equivalent models. To address this issue, this paper proposes a data-driven approach for dynamic equivalent modeling of doubly-fed wind farms. Firstly, the mathematical model of the doubly-fed wind turbine units is simplified into a set of equations. Secondly, neural network components with similarity to these equations are selected and reasonably combined to construct a residual combination neural network. This network includes feature memory layers, information flow acceleration layers, and data relationship mapping layers, aimed at simplifying the detailed wind farm model equivalently. Subsequently, genetic algorithms are employed to optimize the main parameters of the three components in this combined network. A typical wind farm in Henan Province is used as an example for test analysis. The results show that the proposed method based on residual combination neural networks accurately characterizes the output response characteristics of wind farms, achieving higher modeling precision.