Title :
Wind Speed and Rotor Position Sensorless Control for Direct-Drive PMG Wind Turbines
Author :
Qiao, Wei ; Yang, Xu ; Gong, Xiang
Author_Institution :
Dept. of Electr. Eng., Univ. of Nebraska-Lincoln, Lincoln, NE, USA
Abstract :
This paper proposes a wind speed and rotor position sensorless control for wind turbines directly driving permanent magnetic generators (PMGs). A sliding-mode observer is designed to estimate the rotor position of the PMG by using the measured stator currents and the commanded stator voltages obtained from the control scheme of the machine-side converter of the PMG wind turbine. The rotor speed of the PMG (i.e., the turbine shaft speed) is estimated from its back electromotive force using a model adaptive reference system observer. Based on the measured output electrical power and estimated rotor speed of the PMG, the mechanical power of the turbine is estimated by taking into account the power losses of the wind turbine generator system. A back-propagation artificial neural network is then designed to estimate the wind speed in real time by using the estimated turbine shaft speed and mechanical power. The estimated wind speed is used to determine the optimal shaft speed reference for the PMG control system. Finally, a sensorless control is developed for the PMG wind turbines to continuously generate the maximum electrical power without using any wind speed or rotor position sensors. The validity of the proposed estimation and control algorithms are shown by simulation studies on a 3-kW PMG wind turbine and are further demonstrated by experimental results on a 300-W practical PMG wind turbine.
Keywords :
angular velocity control; backpropagation; control system synthesis; electric potential; model reference adaptive control systems; neurocontrollers; observers; permanent magnet generators; position control; power generation control; sensorless machine control; variable structure systems; wind turbines; back electromotive force; backpropagation artificial neural network; direct-drive permanent magnetic generators; machine-side converter; mechanical power; model adaptive reference system observer; optimal shaft speed reference; output electrical power; power 3 kW; power 300 W; power losses; rotor position sensorless control; sliding-mode observer; stator currents; stator voltages; turbine shaft speed; wind speed control; wind turbine generator system; Observers; Rotors; Sensorless control; Shafts; Wind speed; Wind turbines; Artificial neural network (ANN); back electromotive force (EMF); permanent-magnetic generator (PMG); sensorless control; sliding-mode observer; wind turbine;
Journal_Title :
Industry Applications, IEEE Transactions on
DOI :
10.1109/TIA.2011.2175877