• Establish the MPPT with optimal speed ratio combined with hidden layer recurrent neural network (HLRNN) to obtain faster convergence. • Optimize the PI controller gains by using a hidden layer recurrent neural network. • Investigate the active power behavior in the presence of unbalanced grid faults. • Amelioration of the response time of the produced power compared with some other existing neural network schemes. This paper suggests the Hidden Layer Recurrent Neural Network (HLRNN) for controlling a wind power generation system based on a dual-fed induction generator. The generator's stator is connected directly to the electrical grid, while the rotor is linked through bidirectional converters. A PI controller-based Indirect Vector Control (IVC) scheme has been established to pilot the system. The PI regulator allows linear systems to perform well, but when subjected to physical variation conditions, the system's response becomes unstable, making the PI controller insufficient. This paper aims to ensure the PI controller gains self-adaptation regardless of the severity of the circumstances. The effectiveness of the proposed control is demonstrated by taking into account critical conditions such as wind speed changes, generator parameter variations, and asymmetrical faults in the grid. Furthermore, it is confirmed by the enhanced performance and the reduced oscillations during a voltage dip. The simulation results, achieved using MATLAB/Simulink, demonstrate that the response time is reduced to 1.8 (m s), the static error is minimized to 0.16%, and the overshoot is improved to 0.24% when compared to the IVC based on the PI controller and some existing neural network schemes.