AI 中文总结
针对并网逆变器网络，开发时域小信号稳定性认证框架，利用聚类分辨率，通过局部信息验证条件得网络级证书，无需全局特征值计算，能提供诊断信息，涵盖分散到集中多种稳定性证书。
AI 中文摘要
大规模电网常按地理、所有权或控制权限组织，使得需要完整全局模型的稳定性证书颇具挑战。本文为具有可选聚类分辨率的并网逆变器网络开发了一种时域小信号稳定性认证框架。目标是在网络组织和运行的相同规模上认证稳定性：每个聚类使用聚类内和有限边界信息验证条件，这些检查共同产生网络级稳定性证书，无需全局特征值计算。在相位凝聚同步运行点线性化后，小角度近似将模型分解为电压子系统和角频率子系统，后者通过使用对称加权拉普拉斯网络结构的能量论证进行认证。对于电压子系统，引入节点到节点增益，循环小增益论证产生一系列从完全分散到基于聚类和集中式的充分指数稳定性证书。对于任意网络分区，每个聚类验证聚类内有向循环条件和聚类间路径条件。单节点和单聚类极限分别恢复分散式和集中式证书。所得稳定性指标通过将极限裕度定位到单个节点、内部反馈回路和聚类间通道，提供超出通过/失败判定的诊断信息。
英文摘要
Large-scale power networks are often organized by geography, ownership, or control authority, making stability certificates that require a fully assembled global model challenging. This paper develops a time-domain small-signal stability certification framework for grid-forming inverter networks with selectable clustering resolution. The objective is to certify stability at the same scale at which the network is organized and operated: each cluster verifies conditions using intra-cluster and limited boundary information, and these checks collectively yield a network-level stability certificate without requiring a global eigenvalue computation. After linearization about a phase-cohesive synchronized operating point, a small-angle approximation decomposes the model into a voltage subsystem and an angle-frequency subsystem, where the latter is certified by an energy argument using the symmetric weighted-Laplacian network structure. For the voltage subsystem, node-to-node gains are introduced and a cyclic small-gain argument yields a family of sufficient exponential stability certificates ranging from fully decentralized to cluster-based and centralized. For an arbitrary network partition, each cluster verifies intra-cluster directed-cycle conditions and inter-cluster path conditions. The singleton- and single-cluster limits recover the decentralized and centralized certificates, respectively. The resulting stability indices provide diagnostic information beyond a pass/fail verdict by localizing the limiting margin to individual nodes, internal feedback loops, and inter-cluster channels.
