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

Ali Mohamed Eltamaly, Mamdooh Al-Saud, Khairy Sayed and Ahmed G. Abo-Khalil
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Ali Mohamed Eltamaly: Sustainable Energy Technologies Center, King Saud University, Riyadh 11421, Saudi Arabia
Mamdooh Al-Saud: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Khairy Sayed: Electrical Engineering Department, Faculty of Engineering, Sohag University, Sohag 82524, Egypt
Ahmed G. Abo-Khalil: Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia

Sustainability, 2020, vol. 12, issue 9, 1-14

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.

Keywords: DFIG; sensorless; MPPT; speed estimation; opposition-based learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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