Sensorless Active and Reactive Control for DFIG Wind Turbines Using Opposition-Based Learning Technique

Abstract

In this paper, a wind speed sensorless control method for doubly-fed induction generator (DFIG) control in wind energy systems is proposed. This method is based on using opposition-based learning (OBL) in optimizing the parameters of the support vector regression (SVR) algorithm. These parameters are tuned by applying particle swarm optimization (PSO) method. As a general rule, wind speed measurements are usually done using an anemometer. The measured wind speed by the anemometer is taken at the level of the blades. In a high-power wind turbine, the blade diameter is very large which makes the measurement of the wind speed at a single point inaccurate. Moreover, using anemometers also increases the maintenance cost, complexity and the system cost. Therefore, estimating the wind speed in variable speed wind power systems gives a precise amount of wind speed which is then used in the generator control. The proposed method uses the generator characteristics in mapping a relationship between the generated power, rotational speed and wind speed. This process is carried on off-line and the relationship is then used online to deduce the wind speed based on the obtained relationship. Using OBL with PSO-SVR to tune the SVR parameters accelerates the process to get the optimum parameters in different wind speeds.