Data-Driven Online Stability Monitoring of Grid-Following Inverters in Weak Grid

Abstract

This article proposes a data-driven online stability monitoring method using real-time output currents. It contributes to stability judgement for grid-following inverters in weak grid. The mainstream approach relies on impedance measurement, which has a tradeoff between disturbance injection magnitude and duration and measurement accuracy. In contrast, the proposed approach, which is the first to use artificial intelligence technology for stability monitoring of inverters based on real-time current data, enables rapid, and accurate stability assessment without requiring disturbance injection, thus preserving normal inverter operation. The single variate of output currents is skillfully expanded to incorporate multiple features and a novel multiaspect feature fusion (MAFF) model is designed to extract these expanded features. Experimental results demonstrate that the proposed method achieves a sample loading interval of 10 ms, an average classification accuracy of 98%, and an average alarm delay of less than 27 ms. Furthermore, the trained MAFF model shows strong adaptability in wideband oscillation monitoring and can effectively accommodate grid-following inverters with different parameters.