This letter proposes a data-driven inertia estimator for inverter-based resources (IBRs) with grid-forming control. It is able to track both constant and time-varying inertia. By utilizing the Thevenin equivalent, the virtual frequency inside IBRs is first estimated with only its terminal voltage and current phasor measurements. The virtual frequency and the measurements are then used together to derive the state-space swing equation model. Then, an enhanced adaptive Unscented Kalman filter (EAUKF) is developed to estimate IBR inertia. Numerical results on the modified IEEE 39-bus power system demonstrate that the proposed inertia estimator remarkably outperforms the existing state-of-art methods both in tracking speed and accuracy.

中文翻译：

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基于逆变器的资源的数据驱动时变惯量估计


这封信提出了一种数据驱动的惯性估计器，用于具有电网形成控制的基于逆变器的资源（IBR）。它能够跟踪恒定惯性和随时间变化的惯性。通过利用戴维南等效，首先仅通过其端电压和电流相量测量来估计 IBR 内的虚拟频率。然后将虚拟频率和测量结果一起用于推导状态空间摆动方程模型。然后，开发了增强型自适应无迹卡尔曼滤波器（EAUKF）来估计 IBR 惯性。改进后的 IEEE 39 总线电力系统的数值结果表明，所提出的惯性估计器在跟踪速度和精度方面都明显优于现有的最先进方法。