Enhancing the backstepping control approach competencies for wind turbine systems using a dual star induction generator

The goal of this study is to present a novel and improved backstepping control (BC) technique for a dual-star induction generator (DSIG) powered by a wind turbine. This approach relies on the ant lion optimization (ALO), which is employed to determine the optimal parameters of the BC approach and improve the performance of the wind conversion energy system. The ALO approach enhances the robustness of the DSIG, enabling faster dynamic responses, greater accuracy, and consistently improved effectiveness. The fitness function of the ALO approach integrates both integral time absolute error and integral time squared error criteria, ensuring the fulfillment of effectiveness objectives. The performance of the BC-ALO approach is validated through MATLAB. The results of the tests show that the new approach reduces total harmonic distortion, minimizes stator energy fluctuations, and improves dynamic efficiency compared to the BC approach. Additionally, the method can handle uncertainties in model parameters, making it versatile and practical. Simulation results show that the BC-ALO method reduces the total harmonic distortion value compared to the BC method by percentages estimated at 29.45%, 50.44%, and 43.10% in all tests. Also, this approach improves the overshoot value of DSIG power compared to the traditional BC strategy by an estimated 100% in all tests. The proposed approach improves the response time value of the reactive power compared to the conventional BC strategy by percentages estimated at 97.65%, 97.78%, and 95.23% in all tests. The DC link voltage ripples are low if the proposed approach is used, with ratios estimated at 63.31%, 71.38%, and 71.89% in all tests. These results make the proposed approach interesting in other applications such as photovoltaic systems.