Modeling and Stability Analysis of Self-Evolving Models with Small Sample Data Based on Grid-Forming Converter

Although the traditional stability theory based on synchronous motor characteristics has been improved and validated for over a century, it has limitations in dealing with the rapid changes brought by renewable energy sources and high-power electronic devices in modern power systems. Research into supporting technologies has become an indispensable element in the evolution of the new power system, including control algorithms designed for power electronic devices, energy storage options, and advanced intelligent control technologies. This paper proposes a multi-task learning system for small sample data learning and provides an algorithm for processing and classifying oscillation sample data, optimizing the data sample. In addition, the GFM converter impedance angle model and temporal model are established, and the conditions for stable operation of the GFM converter are analyzed. Finally, the conditions for stable operation of the system are verified thoverrough simulation.

I. Introduction

The integration of large-scale renewable energy sources and the re-electrification of demand-side loads are leading to the incorporation of a wide variety of power generation, consumption, and energy storage systems equipped with power electronics into the current electrical grid. This shift is propelling the rapid evolution of power systems towards higher shares of renewable energy and increased use of power electronic devices. However, while this rise in new energy integration enhances the flexibility and controllability within the system, it also introduces new challenges related to stability due to the growing complexity of the grid.