Intelligent Backstepping Control of Variable Speed DFIG-Wind Turbine Under Unbalanced Grid Voltage Conditions Using Genetic Algorithm Optimization
This paper deals with the problem of controlling a Doubly Fed Induction Generator (DFIG) coupled to a wind turbine to enhance the wind integration under unbalanced grid operations. Previous works generally designed the control for DFIG based on standard models assuming that the iron losses are neglected. Also, the main negative aspect of the use of DFIGs is their large sensitivity to grid disturbances, principally to asymmetrical voltage dips. Moreover, in most controllers it is required that wind speed distribution along the turbine blades is known. The contribution of this paper is to present a novel nonlinear control scheme using an intelligent Backstepping approach implemented in both the dq+ and dq- reference frames rotating to achieve an optimal varying speed control under unbalanced grid operations involving online wind speed estimation and the iron losses. The control objective is threefold: (i) tracking the maximum available wind power to ensure the achievement of maximum power point (MPPT) based on the wind speed estimate; (ii) keeping a safe operation of the DFIG and wind speed estimator during unbalanced grid voltage conditions; (iii) efficient tuning based on Genetic Algorithm to determine the optimal parameters of the Backstepping controller. The performances of the control laws are validated by simulation studies on a 4 kW DFIG wind generation system during steady-state and transient grid unbalance using Matlab/Simulink® environment.
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