随着风电机组渗透率的提高，电力系统惯量水平逐年下降。同时，频率响应存在分区特性，以区域为单位评估电力系统惯量更加灵活和准确。因此，提出了一种基于一维压缩激励残差神经网络（one-dimensional squeeze and excitation residual neural network， 1D-SE-ResNet）的系统动态分区惯量评估方法。首先，计算频率曲线趋势和数值近似距离，采用k-means聚类方法对系统进行动态分区并由S-C指标确定分区数量。然后，通过增加压缩和激励模块对一维残差神经网络进行改进，为每个通道提供权重从而提升网络性能，采集系统不同惯量水平和负荷扰动下的区域簇中心节点频率和频率变化率数据作为一维特征输入，区域有效惯量为输出，训练网络实现区域惯量评估。最后，在含风电的IEEE 39和IEEE 118系统上进行仿真。结果表明，在动态分区的基础上，训练好的1D-SE-ResNet可实现区域惯量的准确评估。

As the penetration rate of wind turbines increases, the power system inertia level decreases year by year. At the same time, the frequency response has partition characteristics, and it is more flexible and accurate to evaluate the power system inertia in terms of region. Therefore, a dynamic partition inertia estimation method is presented based on one-dimensional squeeze and excitation residual neural network （1D-SE-ResNet）. Firstly, the trend and value approximation of frequency distance are computed, and the k-means clustering method is used to dynamically partition the system and the number of partitions is determined by the S-C metric. Then, the 1D-ResNet is improved by adding the squeeze and excitation module, which enables it to provide weights for each channel to enhance the network performance. The regional cluster centre node frequency and frequency change rate data under different inertia levels and load perturbations of the system are collected as one-dimensional feature inputs, and the regional effective inertia is the output, training the network to achieve regional inertia estimation. Finally, simulations are carried out on the IEEE 39-node and IEEE 118- node systems containing wind power. The results show that the trained 1D-SE-ResNet can achieve accurate evaluation of regional inertia based on dynamic partitioning.