This chapter presents a sensorless control technique of wind speed for controlling wind-driven doubly fed induction generators (DFIGs) energy systems. A concept behind this technique is to use opposition-based learning (OBL) to optimize the parameters of support vector regression (SVR) system. The parameters of SVR are adjusted using a particle swarm optimization (PSO) approach. An anemometer measures wind speed in most cases. Anemometer readings are obtained at the blade level to determine wind speed. Because of huge blade of a high-power wind turbine, the wind speed at a certain place is difficult to determine. Additionally, the complexity, difficulty of maintenance, and expense of the system all rise with the use of anemometers. Because of this, an accurate wind speed value is provided by wind speed calculation in variable wind speed energy systems used for driving DFIG. The suggested algorithm uses the characteristics of DFIG to map the relation among wind speed, produced power of generator, and rotational speed of generator. This procedure is completed offline, and Next, Wind speed is calculated online using this relationship. It takes less time to find the ideal settings for varying wind speeds when PSO-SVR and OBL work together to modify the SVR’s settings.