A Sensorless Wind Speed and Rotor Position Control of PMSG in Wind Power Generation Systems

Ahmed G. Abo-Khalil, Ali M. Eltamaly, Praveen R.P., Ali S. Alghamdi and Iskander Tlili
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Ahmed G. Abo-Khalil: Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia
Ali M. Eltamaly: Saudi Electricity Company Chair in Power System Reliability and Security, King Saud University, Riyadh 11421, Saudi Arabia
Praveen R.P.: Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia
Ali S. Alghamdi: Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia
Iskander Tlili: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam

Sustainability, 2020, vol. 12, issue 20, 1-19

Abstract: Currently, among the topologies of wind energy conversion systems, those based on full power converters are growing. The permanent magnet synchronous generator (PMSG) uses full power converter to allow wide speed ranges to extract the maximum power from the wind. In order to obtain efficient vector control in a synchronous generator with permanent magnets, it is necessary to know the position of the rotor. The PMSGs work over a wide range of speed, and it is mandatory to measure or estimate their speed and position. Usually, the position of the rotor is obtained through Resolver or Encoder. However, the presence of these sensor elements increases the cost, in addition to reducing the system’s reliability. Moreover, in high wind power turbine, the measured wind speed by the anemometer is taken at the level of the blades which makes the measurement of the wind speed at a single point inaccurate. This paper is a study on the sensorless control that removes the rotor position, speed sensors and anemometer from the speed control. The estimation of the rotor position is based on the output of a rotor current controller and the wind speed estimator is based on the opposition-based learning (OBL), particle swarm optimization and support vector regression.

Keywords: PMSG; wind speed estimation; OBL; support vector machine (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 (3)

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