GA-Based Optimal LQR Controller to Improve LVRT Capability of DFIG Wind Turbines

Nowadays, the doubly-fed induction generators (dfigs) based wind turbines (wts) are the dominant types of wts connected to grid. traditionally the back-to-back converters are used to control the dfigs. in this paper, an indirect matrix converter (imc) is proposed to control the generator. compared with back-to-back converters, imcs have numerous advantages such as: higher level of robustness, reliability, reduced size and weight due to the absence of the bulky electrolytic capacitor. according to the recent grid codes it is required that the wind turbines remain connected to the grid during the grid faults and the following voltage dips. this feature is called low voltage ride-through (lvrt) capability. in this paper, the linear quadratic regulator (lqr) controller is used for optimal control of the dfig. the weighting matrices of the lqr are obtained by using the genetic algorithm (ga) technique. with the lqr controller the intention is to improve the lvrt capability of the dfig wind turbines to satisfy the new lvrt requirements. compared to the pi controller, the superiority of the lqr controller in improving the transient stability and lvrt performance of the dfig wind turbines is shown. simulation results confirm the efficiency of the proposed controller.