This study presents a novel application of a hybrid adaptive neuro-fuzzy inference system (ANFIS)-genetic algorithm (GA)-based control scheme to enhance the performance of a variable-speed wind energy conversion system. The variable-speed wind turbine drives a permanent-magnet synchronous generator, which is connected to the power grid through a frequency converter. A cascaded ANFIS-GA controller is introduced to control both of the generator-side converter and the grid-side inverter. ANFIS is a non-linear, adaptive, and robustness controller, which integrates the merits of the artificial neural network and the FIS. A GA-based learning design procedure is proposed to identify the ANFIS parameters. Detailed modelling of the system under investigation and its control strategies are demonstrated. For achieving realistic responses, real wind speed data extracted from Zaafarana wind farm, Egypt, are considered in the analyses. The effectiveness of the ANFIS-GA controller is compared with that obtained using optimised proportional–integral controllers by the novel grey wolf optimiser algorithm taking into consideration severe grid disturbances. The validity of the ANFIS-GA control scheme is verified by the extensive simulation analyses, which are performed using MATLAB/Simulink environment. With the ANFIS-GA controller, the dynamic and transient stability of grid-connected wind generator systems can be further enhanced.