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Parameter Robustness Enhanced Deadbeat Control for DFIG with ESO-Based Disturbance Estimation
Doubly fed induction generators (DFIGs) are widely applied in wind energy conversion systems, where the harsh service environment and long-lasting operation can bring about motor parameter deviations, deteriorating the system performance. In this paper, an extended state observer (ESO)-based deadbeat control strategy that enhances the system parameter robustness is proposed. Firstly, the effects of motor parameter inaccuracy are analyzed to reflect the control errors and degradation of the system performance. Secondly, a lumped disturbance represented by an additional state extended from the system mathematical model is derived with the parameter inaccuracy taken into consideration. Finally, the parameter robustness enhanced deadbeat control method with the ESO-based disturbance estimation is developed to realize accurate prediction and control, even when the inductance of DFIG deviates under various operation conditions. To verify the effectiveness of the proposed method, simulations are carried out in MATLAB/Simulink for a 1.5 MW DFIG with a 30% stator and rotor inductance deviation. Compared to the conventional control method, smooth and fast dynamic performance is maintained, and the current ripple for the proposed control strategy can be reduced by approximately 40%, where the steady-state tracking performance and parameter robustness of the system are significantly enhanced.
Parameter Robustness Enhanced Deadbeat Control for DFIG with ESO-Based Disturbance Estimation
Doubly fed induction generators (DFIGs) are widely applied in wind energy conversion systems, where the harsh service environment and long-lasting operation can bring about motor parameter deviations, deteriorating the system performance. In this paper, an extended state observer (ESO)-based deadbeat control strategy that enhances the system parameter robustness is proposed. Firstly, the effects of motor parameter inaccuracy are analyzed to reflect the control errors and degradation of the system performance. Secondly, a lumped disturbance represented by an additional state extended from the system mathematical model is derived with the parameter inaccuracy taken into consideration. Finally, the parameter robustness enhanced deadbeat control method with the ESO-based disturbance estimation is developed to realize accurate prediction and control, even when the inductance of DFIG deviates under various operation conditions. To verify the effectiveness of the proposed method, simulations are carried out in MATLAB/Simulink for a 1.5 MW DFIG with a 30% stator and rotor inductance deviation. Compared to the conventional control method, smooth and fast dynamic performance is maintained, and the current ripple for the proposed control strategy can be reduced by approximately 40%, where the steady-state tracking performance and parameter robustness of the system are significantly enhanced.
Parameter Robustness Enhanced Deadbeat Control for DFIG with ESO-Based Disturbance Estimation
Kai Ni (author) / Haochen Shi (author) / Jin Zhang (author) / Chong Zhang (author) / Hongzhe Wang (author) / Yizhou Sun (author)
2023
Article (Journal)
Electronic Resource
Unknown
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