Enhancing Transient Stability of Power Synchronization Control via Deep Learning

Transient stability of grid-connected converters has become a critical concern to the continuing integration of power electronic converters into the power system. Therefore, this paper aims to study the transient stability of power synchronization control (PSC) and propose a developed control system by employing deep learning methods. A long short-term memory (LSTM) network has been trained and integrated into PSC for adapting the synchronization loop of the converter to the grid transients. The trained LSTM network extracts and predicts the voltage trajectory of the connection point with respect to the converter dynamics and the grid strength. In the developed control system, active power reference and internal voltage of the converter are updated dynamically to both satisfy the low voltage ride through (LVRT) requirements and prevent the loss of synchronization. The developed control system is validated by time-domain simulation results.