Nonlinear Modeling and Identification of Doubly Fed Induction Machines Under Varying Grid Conditions

Abstract

Historically, grid-connected synchronous machines have formed the grid voltage and frequency. Today, the high penetration of inverter-based resources poses challenges to grid stability, as conventional grid-following control methods do not provide grid-forming capabilities. New grid-forming control methods need to stabilize the grid. Therefore, electromagnetic transient modeling is essential for control design and stability analysis in future power systems. This article proposes a novel reduced-order modeling and identification approach for doubly fed induction machines (DFIMs) with a grid-connected stator and inverter-connected rotor. The proposed generic modeling remains valid under varying grid or stator conditions. Consequently, the modeling approach is also applicable to induction machines with an inverter-connected stator. In this article, DFIM measurements identify a holistic current-to-flux mapping to model nonlinear magnetic saturation effects. A virtual current injection method is introduced to identify all differential inductances without additional measurements. Various simplified and holistic nonlinear modeling approaches are compared, and measurements validate the proposed holistic flux dynamics model under varying grid conditions.