Intelligent stability monitoring and improvement of grid‐connected converter under weighted average control

This article presents an intelligent stability monitoring and improvement method for the grid‐connected converter system. The model of grid‐connected converter, based on the weighted average current feedback (WACF) and weighted average voltage feedforward (WAVF) control, is first established. Then, the time‐varying grid impedance and parameter perturbation of LCL‐filter are precisely identified by artificial neural network (ANN) module in real time. Furthermore, the control parameters are adaptively tuned by certain rules based on the predicted parameters to increase the high‐frequency stability margin of converter system. Simulation and experimental results are given to validate the proposed identification and parameter tuning method. The proposed method is able to monitor the real‐time operation state of the grid‐connected converter and improve the self‐adaptivity of the grid‐connected converter system against parameter perturbation.