LSTM Data-Driven Model of Multi-scene Virtual Synchronous Generator

Abstract

This paper proposes a data-driven modeling approach using LSTM to accurately describe the dynamic characteristics of Virtual Synchronous Generator (VSG) systems. VSG technology allows grid-connected inverters to resemble synchronous generators externally. However, the commonly used small-signal model faces challenges in capturing the complexity of VSG systems, particularly in complex scenarios. The proposed LSTM-based data-driven model considers the impact of irrational factors, providing accurate and stable VSG system modeling. Experimental results demonstrate the superiority of the LSTM neural network-based data-driven VSG model over the small-signal model and typical data-driven models in terms of accuracy and stability.