In this study, a sensorless vector control for the rotor-tied doubly fed induction generator (RDFIG) is proposed in the grid-connected mode. The proposed sensorless vector control method includes a slip speed/angle estimator which is based on the association of the high-order sliding mode observer (HOSMO) with a phase-locked loop (PLL). In addition, an extensive comparison between the PLL-based HOSMO estimator and the PLL-based second-order sliding mode observer (SMO) estimator is also presented. The Lyapunov stability criterion is used to determine both observer gains to allow their convergence in finite time. Both the proposed HOSMO and the SMO use the three-phase stator current and back-electromotive force (EMF) as state variables which enable the start of the estimation even before the machine is connected to the grid. The proposed HOSMO takes into account the dynamics of the back-EMF space vector. The PLL is used to extract the estimated slip speed/ angle from the estimated back-EMF. The performance of the proposed sensorless vector control strategy is validated experimentally with a 5.5-kW custom-designed RDFIG on a test bench based on the National Instrument (NI) PXIe-8115 realtime controller.

In this study, a sensorless vector control strategy of a rotor-tied doubly fed induction generator (RDFIG) system based on the association of the phase-locked loop (PLL) estimator and the super-twisting sliding mode observer (STSMO) is proposed and analysed. The proposed sensorless control strategy utilises the stator current and the back-electromotive force (EMF) as state variables. The advantage of the proposed sensorless control strategy is that the estimation process is conducted before the machine is connected to the grid. The slip angle/speed is extracted from the estimated back-EMF space vector by using a PLL. The parameters of the STSMO are selected such that the Lyapunov stability criteria are fulfilled and that fast response is achieved. The robustness of the proposed position/speed sensorless control strategy is validated through steady state and dynamic experimental measurements on a 5.5 kW custom-designed grid-connected mode RDFIG test bench based on a National Instruments PXIe-8115 controller.

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A sliding mode control-based model reference adaptive system (SMC-MRAS) estimator for sensor-less control of doubly fed induction generator (DFIG) systems in wind turbine applications is proposed in this paper. The proposed SMC-MRAS estimator uses the rotor current as a variable of interest. The proposed SMC-MRAS estimator has the advantage of being immune to machine parameter variations. The SMC parameters are designed using the Lyapunov stability criteria. The performance of the proposed SMC-MRAS estimator is validated using simulations in MATLAB/SIMULINK. A comparative study between the proposed SMC-MRAS estimator and the PI-MRAS estimator is also conducted to demonstrate the superiority of the proposed SMC-MRAS estimator.