Ant Colony Optimization-Tuned Adaptive Backstepping Control for Power Regulation in Multi-Machine Wind Turbine Systems with Five-Phase Permanent Magnet Synchronous Generators

This study proposes a hybrid ant colony optimization–backstepping control (ACO-BC) strategy for a multi-machine wind energy conversion system based on five-phase permanent magnet synchronous generators (PMSGs). The proposed strategy is developed to enhance the dynamic response, power regulation, robustness, and power quality of the system under variable wind-speed conditions. The ACO-ABC mechanism is employed to optimize the controller parameters, thereby reducing power fluctuations and improving the dynamic characteristics of the controlled system. The designed strategy is comprehensively evaluated against a developed sliding-mode control (DSMC) strategy under two distinct wind-speed operating scenarios using MATLAB 2021/Simulink. The simulation results demonstrate significant performance improvements with the proposed ACO-BC strategy. Under the first test, the active-power ripple and response time are reduced by 42.85% and 34.19%, respectively, compared with DSMC, while the active-power steady-state error (SSE) is reduced by 33.33%. Moreover, the current total harmonic distortion (THD) is reduced from 7.63% with DSMC to 2.32% with ACO-ABC, corresponding to a substantial reduction of 69.59%. Under the second test, the proposed strategy reduces the active-power ripple, response time, and SSE by 50%, 22.76%, and 66.67%, respectively, compared with DSMC. In addition, ACO-BC maintains the same overshoot level as DSMC while achieving substantial improvements in ripple, SSE, and dynamic response. These results demonstrate that the proposed strategy provides enhanced power regulation, faster dynamic response, reduced fluctuations, and improved current quality under both stepwise and randomly varying wind-speed conditions. Although the ACO-BC strategy involves higher implementation complexity and requires the tuning of multiple controller gains, its overall performance demonstrates its potential as an effective control solution for multi-machine PMSG-based wind energy conversion systems.