Performance enhancement of a wind driven PMSG using an artificial neural network based nonlinear backstepping controller.

With the increasing demand for wind energy in the electric power generation industry, optimizing robust and efficient control strategies is essential for a wind energy conversion system (WECS). In this regard, this study proposes a novel hybrid control strategy for wind power systems directly coupled to a permanent-magnet synchronous generator (PMSG). The contribution of this work is to propose a control strategy design based on a combination of the nonlinear Backstepping approach for system stabilization according to Lyapunov theory and the application of artificial neural network to maximize energy harvesting regardless of wind speed fluctuations. The hybrid control strategy, which is highly efficient in reducing current/torque ripples, as well as the THD ratio of PMSG currents, was applied to achieve good system performance. The overall system is implemented in MATLAB/Simulink to verify the effectiveness of the proposed control technique under varying wind conditions. Analysis of the simulation results for the proposed control versus field-oriented control (FOC) shows that the proposed control strategy exhibits less ripples in the electromagnetic torque, with the ripple ratio decreasing significantly from 32.95% to 19.43%. In addition, the THDs of the stator current decreased from 20.87% to 14.88%, proving the reliability and efficiency of the proposed control strategy compared to FOC. Meanwhile, these results are valuable for the application of the proposed control strategy in a real WECS system.