Eigenvalue-Oriented Data-Driven Small-Signal Stability Assessment for DC Microgrids

Abstract

The stability issues of DC microgrids (DCmGs) are becoming increasingly important due to the widespread deployment of renewable distributed energy resources (DERs), which has led to a growing demand for DCmGs. Existing small-signal white-box and gray-box models of DCmGs require system parameters and are severely limited in practical applicable scenarios, while black-box models cannot effectively assess DCmGs' stability. To this end, this paper proposes a novel eigenvalue-oriented small-signal stability assessment approach for DCmGs, which achieves small-signal modeling and stability assessment of DCmGs through measurements of converters' interface. The contributions of this paper are as follows: 1) The Fed-Koopman network is proposed, mapping a multi-scenario DCmG to high-dimensional linear space via a federated learning algorithm, and establishing the black-box small-signal model. 2) An improved elastic net regression (ENR) algorithm, named NeuENR is proposed, identifying the system matrix $\mathcal {A}_{sys}$. 3) Considering measurement errors during $\mathcal {A}_{sys}$ identification, the small-signal stability criterion and stability margin are defined. The approach is implemented on the commercial electromagnetic transient simulation platform, CloudPSS, demonstrating its applicability to different operation conditions of DCmGs and superior assessment performance.