Mountain Gazelle Algorithm-Based Optimal Control Strategy for Improving LVRT Capability of Grid-Tied Wind Power Stations

The large-scale wind energy conversion systems (WECS) based on a doubly fed induction generator (DFIG) have recently gained attention due to their numerous economic and technological advantages. However, the rapid integration of WECS with standing power networks severely influenced the system's reliability and stability; also, the DFIG rotor circuit experiences a substantial overcurrent due to grid voltage fluctuations. Indeed, these problems emphasize the significance of a DFIG's low-voltage ride-through (LVRT) capacity in maintaining the stability of the electrical grid during voltage fluctuations. To solve these challenges simultaneously, this research employs a metaheuristic optimization technique to regulate a doubly fed induction generator's (DFIG) operation via a wind turbine (WT) system. The article proposes a novel Mountain Gazelle Optimizer (MGO) to optimize the proportional-integral (PI) controller gains for the DFIG system's active and reactive power control to enhance the LVRT capability of Wind turbines linked to the power grid. In the proposed scheme, LVRT improvement is proportional to undershoot or overshoot, settlement time, and steady-state inaccuracy of voltage responses. The proposed control method is implemented in MATLAB by a detailed model of 9MW wind turbine, and its performance is validated and compared with traditional optimization control approaches. The suggested MGO method's efficacy is demonstrated by the assessment and comparison to classic optimization-based PI controllers under various fault scenarios. The simulation results show that the optimized control method improved performance in terms of three-phase terminal voltage output responses, active power, reactive power demand to networks, and DC-Link voltage.