Probabilistic Robust Damping Controller Optimization for Mitigating Subsynchronous Control Interaction in DFIG and PMSG‐Based Wind Farms

Abstract

Advanced control and mitigation solutions are required due to the enormous challenges posed by sub‐synchronous control interaction in wind farms. These concerns include system instability, potential damage to turbines, and grid stability issues. This research presents the probabilistic robust damping controller, an adaptive control approach, to address the issues in grid‐connected wind farms. The innovation employs the balanced remora optimization algorithm to optimize controller parameters, ensuring system stability under various conditions. For permanent magnet synchronous generator‐based wind farms with weak grid circumstances, critical elements such as the DC‐link voltage, the d‐q axis current reference, and the actual values in the grid‐side converter are essential for problem mitigation. The controller generates controlled output using these input signals. The MATLAB‐based implementation demonstrates the controller effectiveness in damping issues within a series‐compensated doubly fed induction generator. Comparative analysis with conventional controllers under different grid conditions and disturbances validates its superior performance. Time‐domain simulations, both with and without time delay, further confirm the controller's ability to enhance system stability and dampen the issues. Four schemes are used to evaluate the doubly fed induction generator‐based wind farms: wind speed at 13 m/s with 75% series compensation, 7 m/s with 75% compensation, 9 m/s with 45% compensation, and 9 m/s with 60% compensation. Under short circuit ratio, the wind farms based on permanent magnet synchronous generators are analyzed in three scenarios: normal, strong, and weak cases.