Employing Machine Learning for Enhancing Transient Stability of Power Synchronization Control During Fault Conditions in Weak Grids

Grid-connected converters are exposed to the loss of synchronisation with grid during severe voltage sags particularly when operating under weak condition which introduces and frequency volatility. This paper presents employing machine learning methods besides modifying converter control scheme enhance transient stability power synchronization (PSC). For early detection instability PSC provide adequate time for taking correcting actions, an encoder stacked classifier is proposed trained be robust against data corruption added noise. Then, by integrating loop PSC, a phase freezing mode introduced avoid losing synchronism faults. It disclosed that frozen loop, activated scheme, can ensure PSC. Time-domain simulations conducted confirm presented findings.