Robust Predictive Control for Direct-Driven Surface-Mounted Permanent-Magnet Synchronous Generators Without Mechanical Sensors

• Published in: IEEE transactions on energy conversion Vol. 33; no. 1; pp. 179 - 189
• Main Authors: Abdelrahem, Mohamed, Hackl, Christoph M., Zhang, Zhenbin, Kennel, Ralph
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Coders; Delays; Disturbance observers; Encoderless control; Extended Kalman filter; Harmonic distortion; Observers; Parameter estimation; Permanent magnets; permanent-magnet synchronous generator; Position sensing; Predictive control; Robust control; Robustness; Rotor speed; Rotors; Sensors; Stators; Torque; Transducers; variable-speed wind turbines; Wind turbines
• ISSN: 0885-8969; 1558-0059
• DOI: 10.1109/TEC.2017.2744980

In this paper, an extended Kalman filter (EKF) is employed to estimate stator currents, rotor speed, rotor position, and mechanical torque of a direct-driven surface-mounted permanent-magnet synchronous generator (PMSG) based variable-speed wind turbine systems controlled by a robust predictive deadbeat (DB) algorithm. Therefore, the flux-oriented control of the PMSG is realized without mechanical sensors (i.e., no position encoders or speed transducers). The estimated stator currents are fed back to the prediction model in order to compensate the one-step delay caused by the digital controller and to reduce the total harmonic distortion of the stator currents. Thus, the torque ripples are also reduced. Furthermore, a simple disturbance observer is presented, which estimates the perturbations caused by parameter variations of the PMSG or by any unmodeled dynamics in order to enhance the robustness of the proposed DB algorithm. The proposed scheme is experimentally validated using a 14.5-kW PMSG and a dSPACE DS1007 real-time platform. Its performance is compared with that of the conventional DB control strategy for all operation conditions and under parameter variations of the PMSG.