Adaptive Deep Artificial Neural Network-Based Transient Stability Enhancement of Grid-Connected SCIG Under Fault and Harmonic Disturbance
One of the impacts of increasing the penetration of wind energy into the power systems of the future is the rise in transient stability and power quality problems, especially in the case of squirrel cage induction generator (SCIG) based wind turbines connected to the grid. Since SCIG systems lack autonomous excitation control and connect directly to the grid, they are very susceptible to voltage dips, short, circuit faults, and harmonic disturbances. In this article, an adaptive deep artificial neural network (ANN), based control framework is suggested to improve transient stability and harmonic tolerance of grid, connected SCIG during harsh disturbance situations. A complete dynamic model of the SCIG is made in the synchronous reference frame, where the electromechanical coupling, reactive power dynamics, and harmonic distortion effects are considered. To help controller training, a multi, objective performance index that limits rotor speed variation, voltage sag severity, and total harmonic distortion (THD) is developed. The deep ANN that is proposed here uses multi, layer nonlinear mapping with adaptive online learning to produce in real time the most suitable reactive compensation commands. The simulation results under severe three, phase faults, harmonic injection, and combined disturbance scenarios reveal the ability to achieve the major enhancements in rotor speed recovery, voltage restoration time, damping performance, and THD reduction compared to conventional PI and shallow ANN controllers. Also, robustness testing with varying parameters shows that the proposed method is effective, flexible, and suitable for wind energy integration into a weak grid.