A Gray-Box Stability Analysis Mechanism for Power Electronic Converters

TL;DR

This work tackles stability assessment of grid-tied power electronic converters when controller topology and parameters are not fully accessible. It introduces a gray-box stability analysis based on dynamic mode decomposition with control (DMDc) that embeds physical state equations into the data-driven framework and uses data stacking to compensate for limited measurements. The approach yields more accurate and interpretable identification of dominant eigenvalues and oscillation modes, closely matching small-signal models and measured waveforms. This enables practical stability monitoring for converters with partial knowledge, with potential extension to nonlinearities and improved physical constraint integration.

Abstract

This paper proposes a gray-box stability analysis mechanism based on data-driven dynamic mode decomposition (DMD) for commercial grid-tied power electronics converters with limited information on its control parameters and topology. By fusing the underlying physical constraints of the state equations into data snapshots, the system dynamic state matrix and input matrix are simultaneously approximated to identify the dominant system dynamic modes and eigenvalues using the DMD with control (DMDc) algorithm. While retaining the advantages of eliminating the need for intrinsic controller information, the proposed gray-box method establishes higher accuracy and interpretable outcomes over the conventional DMD method. Finally, under experimental conditions of a low-frequency oscillation scenario in electrified railways featuring a single-phase converter, the proposed gray-box DMDc is verified to identify the dominant eigenvalues more accurately.

Figures (10)

• Figure 1: System diagram of a single-phase converter in electrified railways.
• Figure 2: Illustration of data collection and data snapshot generation.
• Figure 3: Schematic of black-box dynamic mode decomposition (DMD) and proposed gray-box dynamic mode decomposition with control (DMDc) method (SVD: singular value decomposition, ED: eigen-decomposition).
• Figure 4: Data-stacking technique — data matrices are augmented with higher row dimensions.
• Figure 5: Experimental setup and signal measurements of a single-phase converter in the electrified railway system.