Due to the capability to perform participation factor analysis and oscillation origin location, the state–space model (SSM)-based eigenvalue method has been widely used for stability assessment of inverter-penetrated power systems. However, possible internal confidentiality of inverters impedes the derivation of their SSMs. In addition, conventional derivation procedure of system SSM can be tedious when complicated transmission network topology and various transmission cables are involved, which may result in a high-order system SSM. To this end, this article presents a black box-based incremental reduced-order modeling framework. The reduced-order SSMs of the inverters and transmission cables are extracted from their $dq$ -domain admittance frequency responses and $abc$ -domain impedance frequency responses, respectively, by the matrix fitting algorithm. Then, the SSM operators proposed in this article recursively assemble the components' fitted SSMs in the similar manner as the impedance model operator-based recursive components' impedance aggregation, while preserving the dynamics of individual components. Simulation results show that the presented state–space modeling framework can properly identify the state–space models of black-box devices at component modeling stage, simplify assembling procedure at subsystems/components integration stage, and release computational burden at system participation factor analysis stage.

中文翻译：

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基于黑盒的逆变器电力系统增量降阶建模框架

由于能够执行参与因子分析和振荡原点定位，基于状态空间模型（SSM）的特征值方法已广泛用于逆变器贯穿式电力系统的稳定性评估。然而，逆变器可能的内部机密性阻碍了其 SSM 的推导。此外，当涉及复杂的传输网络拓扑和各种传输电缆时，传统的系统SSM推导过程可能会很繁琐，这可能会导致高阶系统SSM。为此，本文提出了一种基于黑盒的增量降阶建模框架。逆变器和传输电缆的降阶 SSM 是从它们的$dq$ -域导纳频率响应和$abc$ 域阻抗频率响应分别通过矩阵拟合算法得到。然后，本文提出的 SSM 算子以与基于阻抗模型算子的递归组件阻抗聚合类似的方式递归地组装组件的拟合 SSM，同时保留各个组件的动态性。仿真结果表明，所提出的状态空间建模框架可以在组件建模阶段正确识别黑盒设备的状态空间模型，简化子系统/组件集成阶段的组装过程，并在系统参与因子分析阶段减轻计算负担。