A new proportional–integral (PI) controller tuning method for permanent magnet synchronous generator (PMSG) wind energy conversion system based on the recently developed Graylag Goose Optimization (GGO) algorithm is presented in this paper. The control scheme is for the control of the rotor side and grid side converters of a 2 MW back-to-back converter topology. The GGO algorithm optimizes eight PI gains of d–q axis current regulation, DC-link voltage control and reactive power management inspired by the cooperation in foraging of flocks of birds Anser anser and the exchange of leaders in a flock while flying in V-formation. For the aerodynamic power extraction in the variable wind conditions, there is an optimum speed ratio for the wind and the blades, which is incorporated in the present invention as tip speed ratio (TSR) based maximum power point tracking (MPPT) strategy and does not need to measure the wind speed directly. The proposed GGO-PI controller is compared with the well-known Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA) and conventional Ziegler-Nichols (ZN) tuning algorithms with the same stochastic Kaimalspectrum wind profile and 40% grid voltage sag disturbance. The simulation results in MATLAB R2023b/Simulink show that the proposed approach yields a superior dynamic response, improved tracking accuracy, and even better robustness, and the smallest ITAE is 0.0317. The voltage overshoot of the DC-link is decreased by 31.4% compared to the standard methods, and the power ripple is decreased by 27.6% compared to the standard methods. Further, the system total harmonic distortion (THD) at the grid point is kept below 2.31%, which is compliant with the IEEE 519-2022 standard. Results from 30 independent Monte Carlo runs converge within 87 iterations and have the smallest performance variation compared to all the other optimizers tested. The method also has the highest MPPT efficiency of 97.2%, which shows a higher energy capturing capability. These results validate that the proposed GGO-based PI tuning algorithm is a strong, efficient and practically applicable tuning method for grid connected PMSG wind energy systems