Maximum power point tracking of wind turbines with a chaotic-based imperialist competition algorithm

Integrating large-scale wind farms with the main grid poses several challenges to operating the power utilities. One of the most important is to extract the maximum possible power in different operating conditions from the installed capacity. One of the common solutions in wind power plants equipped with doubly-feed induction generators is to control the output power of the rotor by using a proportional-integral controller.In this paper, a modified chaos-based imperialist competition algorithm is used to manage the control coefficients of twenty wind turbines (2 MW) located in a microgrid. The results show that the use of chaos theory improves the quality of convergence tracking of maximum power points during rapid changes in wind speed. In the classical proportional-integral controller, when the wind speed changes rapidly, the generated torque contains a large ripple and this controller is unable to overcome the nonlinear and ripple torque properties. This weakness increases the stress on the system and can damage the structural equipment of the generator. At the same time, the results obtained using the chaotic-based imperialist competition algorithm show that not only the ripple content is significantly reduced, but also more than 40% of its subsurface value is reduced.