Sensorless control for DFIG wind turbines based on support vector regression

In this paper, a sensorless based doubly-fed induction generator (DFIG) control in wind power generation systems is proposed, which is based on the theory of support vector regression (SVR). The inputs of the SVR wind speed estimator are chosen as the wind turbine power and rotational speed. During the offline training, a specified model which relates the inputs to the output is obtained. Then, the wind speed is determined online from the instantaneous inputs. Meanwhile, the DFIG rotor dq-axis currents are controlled to optimize the stator active and reactive power. The stator active power is adjusted in order to extract the maximum power from the wind power. The output reactive power of the wind power conversion system is controlled as zero to keep unity power factor of the stator voltage and current. However, the stator reactive power control is used to optimize the generator efficiency by sharing the reactive power between stator and rotor. The experimental results show the excellent of performance of the power, current and pitch angle controllers in the steady state and transient responses for the different modes and wind speed. The experimental results have verified the validity of the proposed estimation and control algorithms.