并网逆变器阻抗特性是分析逆变器与电网交互稳定性的重要依据，准确获取不同运行点阻抗特性是稳定运行的基础。针对实际测量可覆盖的运行点有限的问题，提出了一种基于深度算子网络结构的并网逆变器多运行点阻抗辨识方法，实现了多运行点阻抗特性的准确表征。首先，面向多运行点阻抗辨识，构建了以主干网络进行频率特征映射、以分支网络表征运行点信息的辨识模型，并在训练阶段构造频段加权损失函数，引入关键频段辨识误差约束，实现了多运行点下阻抗辨识。其次，提出面向参数变化场景的小样本阻抗迁移模型，引入源域预训练模型和目标域微调策略，提高了小样本条件下的阻抗辨识效果。最后，通过算例验证了所提方法能够取得较好的多运行点阻抗辨识效果，并在参数变化和多逆变器并联场景下保持较好的适用性。

Impedance characteristics of grid-connected inverters are an important basis for analyzing inverter-grid interaction stability, and accurately obtaining impedance characteristics under different operating points is essential for stable operation. To address the problem that practical measurements can cover only a limited number of operating points, a multi-operating-point impedance identification method for grid-connected inverters based on a deep operator network structure is proposed to accurately characterize impedance under multiple operating points. Firstly, for multi-operating-point impedance identification, an identification model is constructed in which the trunk network performs frequency feature mapping and the branch network represents operating-point information. During the training stage, a frequency-band-weighted loss function is constructed, and constraints on identification errors in key frequency bands are introduced to achieve impedance identification under multiple operating points. Secondly, a small-sample impedance transfer model for parameter variation scenarios is proposed. A source-domain pretrained model and a target-domain fine-tuning strategy are introduced to improve the impedance identification performance under small-sample conditions. Finally, case studies verify that the proposed method achieves satisfactory multi-operating-point impedance identification performance and maintains good applicability in parameter variation and multi-inverter parallel scenarios.